docs: Documentation for the RESTful API POC

.github/ISSUE_TEMPLATE/bug_report.yaml

@@ -21,25 +21,25 @@ body:
       value: |
         I tried this:
 
-        1. `coyote`
+        1. `loki`
 
         I expected this to happen:
 
         Instead, this happened:
   - type: textarea
-    id: coyote-log
+    id: loki-log
     attributes:
-      label: Coyote log
+      label: Loki log
-      description: Include the Coyote log file to help diagnose the issue. (`coyote --info` to see the log_path)
+      description: Include the Loki log file to help diagnose the issue. (`loki --info` to see the log_path)
       value: |
         | OS      | Log file location                                     |
         | ------- | ----------------------------------------------------- |
-        | Linux   | `~/.cache/coyote/coyote.log`                      |
+        | Linux   | `~/.cache/loki/loki.log`                      |
-        | Mac     | `~/Library/Logs/coyote/coyote.log`                |
+        | Mac     | `~/Library/Logs/loki/loki.log`                |
-        | Windows | `C:\Users\<User>\AppData\Local\coyote\coyote.log` |
+        | Windows | `C:\Users\<User>\AppData\Local\loki\loki.log` |
 
         ```
-        please provide a copy of your coyote log file here if possible; you may need to redact some of the lines
+        please provide a copy of your loki log file here if possible; you may need to redact some of the lines
         ```
 
   - type: input
@@ -57,13 +57,13 @@ body:
     validations:
       required: true
   - type: input
-    id: coyote-version
+    id: loki-version
     attributes:
-      label: Coyote Version
+      label: Loki Version
       description: >
-        Coyote version (`coyote --version` if using a release, `git describe` if building
+        Loki version (`loki --version` if using a release, `git describe` if building
         from main).
-        **Make sure that you are using the [latest coyote release](https://github.com/Dark-Alex-17/coyote/releases) or a newer main build**
+        **Make sure that you are using the [latest loki release](https://github.com/Dark-Alex-17/loki/releases) or a newer main build**
-      placeholder: "coyote 0.1.0"
+      placeholder: "loki 0.1.0"
     validations:
       required: true

.github/workflows/release.yaml

@@ -98,9 +98,9 @@ jobs:
           # Ignore Act's local artifact dir noise
           echo artifacts/ >> .git/info/exclude || true
 
-          # Edit the version line right after name="coyote"
+          # Edit the version line right after name="loki"
           sed -E -i '
-            /^[[:space:]]*name[[:space:]]*=[[:space:]]*"coyote"[[:space:]]*$/ {
+            /^[[:space:]]*name[[:space:]]*=[[:space:]]*"loki"[[:space:]]*$/ {
               n
               s|^[[:space:]]*version[[:space:]]*=[[:space:]]*"[^"]*"|version = "'"$VERSION"'"|
             }
@@ -278,7 +278,7 @@ jobs:
       - name: Verify file
         shell: bash
         run: |
-          file target/${{ matrix.target }}/release/coyote
+          file target/${{ matrix.target }}/release/loki
 
       - name: Test
         if: matrix.target != 'aarch64-apple-darwin' && matrix.target != 'aarch64-pc-windows-msvc'
@@ -382,11 +382,11 @@ jobs:
         shell: bash
         run: |
           # Set environment variables
-          macos_sha="$(cat ./artifacts/coyote-x86_64-apple-darwin.sha256 | awk '{print $1}')"
+          macos_sha="$(cat ./artifacts/loki-x86_64-apple-darwin.sha256 | awk '{print $1}')"
           echo "MACOS_SHA=$macos_sha" >> $GITHUB_ENV
-          macos_sha_arm="$(cat ./artifacts/coyote-aarch64-apple-darwin.sha256 | awk '{print $1}')"
+          macos_sha_arm="$(cat ./artifacts/loki-aarch64-apple-darwin.sha256 | awk '{print $1}')"
           echo "MACOS_SHA_ARM=$macos_sha_arm" >> $GITHUB_ENV
-          linux_sha="$(cat ./artifacts/coyote-x86_64-unknown-linux-musl.sha256 | awk '{print $1}')"
+          linux_sha="$(cat ./artifacts/loki-x86_64-unknown-linux-musl.sha256 | awk '{print $1}')"
           echo "LINUX_SHA=$linux_sha" >> $GITHUB_ENV
           release_version="$(cat ./artifacts/release-version)"
           echo "RELEASE_VERSION=$release_version" >> $GITHUB_ENV
@@ -402,23 +402,23 @@ jobs:
         if: env.ACT != 'true'
         run: |
           # run packaging script
-          python "./deployment/homebrew/packager.py" ${{ env.RELEASE_VERSION }} "./deployment/homebrew/coyote.rb.template" "./coyote.rb" ${{ env.MACOS_SHA }} ${{ env.MACOS_SHA_ARM }} ${{ env.LINUX_SHA }}
+          python "./deployment/homebrew/packager.py" ${{ env.RELEASE_VERSION }} "./deployment/homebrew/loki.rb.template" "./loki.rb" ${{ env.MACOS_SHA }} ${{ env.MACOS_SHA_ARM }} ${{ env.LINUX_SHA }}
 
       - name: Push changes to Homebrew tap
         if: env.ACT != 'true'
         env:
-          TOKEN: ${{ secrets.COYOTE_GITHUB_TOKEN }}
+          TOKEN: ${{ secrets.LOKI_GITHUB_TOKEN }}
         run: |
           # push to Git
           git config --global user.name "Dark-Alex-17"
           git config --global user.email "alex.j.tusa@gmail.com"
-          git clone https://Dark-Alex-17:${{ secrets.COYOTE_GITHUB_TOKEN }}@github.com/Dark-Alex-17/homebrew-coyote.git
+          git clone https://Dark-Alex-17:${{ secrets.LOKI_GITHUB_TOKEN }}@github.com/Dark-Alex-17/homebrew-loki.git
-          rm homebrew-coyote/Formula/coyote.rb
+          rm homebrew-loki/Formula/loki.rb
-          cp coyote.rb homebrew-coyote/Formula
+          cp loki.rb homebrew-loki/Formula
-          cd homebrew-coyote
+          cd homebrew-loki
           git add .
-          git diff-index --quiet HEAD || git commit -am "Update formula for Coyote release ${{ env.RELEASE_VERSION }}"
+          git diff-index --quiet HEAD || git commit -am "Update formula for Loki release ${{ env.RELEASE_VERSION }}"
-          git push https://$TOKEN@github.com/Dark-Alex-17/homebrew-coyote.git
+          git push https://$TOKEN@github.com/Dark-Alex-17/homebrew-loki.git
 
   publish-crate:
     needs: publish-github-release

.gitignore

@@ -3,5 +3,5 @@
 /.env
 !cli/**
 .idea/
-/coyote.iml
+/loki.iml
 /.idea/

.loki-project.json

@@ -0,0 +1 @@
+{"type":"rust","build":"cargo build","test":"cargo test","check":"cargo check","_detected_by":"heuristic","_cached_at":"2026-04-13T13:36:33-06:00"}

CHANGELOG.md

@@ -1,251 +1,3 @@
-## v0.7.2 (2026-06-19)
-
-### Fix
-
-- usql version upgrade
-
-## v0.7.1 (2026-06-19)
-
-### Fix
-
-- sbx mixins must be passed in directories, not as files and the files must be named spec.yaml per new sbx version
-
-## v0.7.0 (2026-06-18)
-
-### Feat
-
-- added configurable cache path via the COYOTE_CACHE_PATH environment variable
-- added a memory option to .set tab completions
-- Added a diagnostic .info tools subcommand to make it easier to see what tools are enabled in all contexts
-- Added additional info outputs for enabled skills and sbx directories
-- directly execute shell commands from within the REPL
-- created mixin kit for built-in functions and MCP servers
-- Added sbx mixins for the secrets providers so users can also bootstrap those as well.
-- added support for loading sbx mixins that are dynamically discovered in the users workspace and config directory
-- Added a --fresh flag to let users create a truly bare bones sandbox without bootstrapping their config
-- initial built-in sandboxing support powered by Docker sbx
-- Added the ability to auto-bootstrap workspace memory when in git repos
-- Added explicit guardrail handling for pending agents
-- auto-append memory to memory index and don't necessarily require the LLM to remember to do it after a write
-- Added an --init-memory [global|workspace] flag to easily and quickly enable memory
-- added memory global configuration settings to the output of --info and .info
-- added .set memory REPL commands to control memory injection and applied formatting
-- Create the built-in memory management tools
-- Append the memory system prompts (readonly or r/w) to the system prompt when applicable
-- Created the --no-memory CLI flag to disable memory for this invocation
-- Added the memory configuration properties and storage to the main app config, roles, sessions, and agents.
-- initial scaffolding of a memory system
-
-### Fix
-
-- rebuild the tool scope after dynamically updating the skills_enabled value in the REPL
-- properly resolve Windows-based local vault password file locations and bootstrap them into the sandbox when possible
-- auto-translation of user-prefixed Mac and Linux paths for the vault password file when running inside a sandbox
-- don't attempt to auto complete .vault list in the REPL; that's the end of the command
-- buffer tool stdout as well as stderr so that any tools that error to stdout are captured and included in the response to the model, enabling the model to see what went wrong and to reason about how to fix it.
-- auto-bootstrapped memory was accidentally putting the MEMORY.md directly in the repo root rather than .coyote/memory/MEMORY.md
-- improved the fs_patch script description and added improved error handling to it.
-- added in forgotten require_max_tokens to the fable model
-- append memory functions to non-graph based agents on init
-- when auto_continue is disabled via the .set auto_continue false command, it should strip the todo functions from the list of functions
-- use rawPredict for non-streaming Claude requests
-
-### Refactor
-
-- Migrated the .skills command completion to use StateFlags and updated the help messages
-
-## v0.6.0 (2026-06-05)
-
-### Feat
-
-- added skill hint prompt injection and configuration
-- Fallthrough on missing secrets during mcp.json merging
-- validate visible_skills field at config load time
-- implemented reflexion (sorta) in sisyphus for significant code changes to delegate to the code-reviewer agent
-- improved explore agent
-- removed conditional fallback of LLM_*_RAW_JSON from built-ins
-- updated enabled_skills handling to support both list and comma-separated strings
-- added new REPL set commands for toggling skills and changing what skills are enabled
-- upgraded to the latest version of mcp-remote
-- fs_grep now works with both files and directories
-- improved code reviewer agents with skills
-- added round trip validation for vault providers to ensure permissions and authentication
-- created new first-time run wizard for secrets provider
-- vault_password_file or nothing at all is shorthand for just using the local gman provider for secret management
-- refactored gman usage to be generic and work with various vault providers and use the SupportedProvider enum directly for configurations
-- created initial parity gman generalization for vault provider
-- Refactored the sisyhpus agent system to utilize the new skills system to improve performance and reliability
-- llm graph nodes support skills
-- updated sisyphus and coder tools
-- removed potentially confusing tab completions for .skill
-- .edit skill <name> support from within the REPL
-- Added skills_dir to the info output of Coyote
-- Created a few auto built-in skills
-- Added support for auto_unload skills during chat
-- cleaned up skill implementation
-- support multiple skill flags to load multiple skills at CLI startup
-- Modified --skill CLI to allow users to specify skills to start the REPL or CLI with.
-- added CLI --skill flag for modifying skills easily
-- REPL integration with skills
-- dynamic loading/unloading of skill tools and MCP servers whenever load_skill/unload_skill are invoked
-- created built-in functions for listing, loading, and unloading skills
-- implemented the skills policy to track available skills per context
-- added remote install and install support for skills
-- created the skill registry
-- decided to make skills persist to disk like agents and not in-memory like built-in roles
-- scaffold skill module
-
-### Fix
-
-- disable skills for specific built-in roles
-- redirect stderr into user's /dev/tty for guards
-- azure doesn't support underscores in key vault
-- accidental regression on enabled_skills being empty = all
-- greedy secrets regex caused multiple secrets on one line to fail
-- add agent context check to skill visibility validation
-- enforced global visible_skills in llm node validation and improved skill loading error handling across the project
-- restore agent skill policy on error during effective policy calculation
-- apply the same validation for skill filenames on list_skills as happens everywhere else
-- the vault's init_bare should try to load the provisioned secret_provider from the config file without also interpolating any of the rest of the configuration file. It should only fail if the user has not yet created a configuration file; i.e. done a first-time run.
-- the vault roundtrip test used characters that are unsupported by some major secrets providers
-- fixed tool filtering logic for skills and user functions in agents
-- privilege leak when unloading skills and leaving tool scope untouched
-- When bootstrapping an app config to interpolate secrets, clone the secrets provider configuration as well so config secrets stored in remote vaults can be used properly
-- forgot to move back up the vault probe value error to be before the delete
-- don't silently fail on skill role composition extraction in llm nodes
-- set -euo pipefail for the temp script in execute_command.sh tool
-- added forgotten skill name validation to has_skill to prevent side-channel attacks
-- use unique values for the secrets round trip verification
-- stop interpolating a line if any errors occur
-- added path validation for skill names
-- effective_policy unconditionally overwrote skill values for role-like structs
-- updated execute_command to not mangle heredocs and also added explicit instructions to the coder and sisyphus agents to use fs_write and fs_patch over execute_command when writing files
-- llm nodes accidentally skipped skill_registry::effective_role because I was passing an inline role instead
-- updated temperature values for all agents and roles
-- added back in require_max_tokens for new Claude models
-- skill support also requires function calling to be enabled
-- non_tty tests break on some TTY terminals
-- skill loading on agents
-- forgot to bootstrap skills on REPL startup
-- remove now deprecated .skill edit command
-
-### Refactor
-
-- removed redundant skill name validation from has_skill function
-- support both CSV and list formats for enabled_tools
-- Support both CSV and list formats for enabled_mcp_servers
-
-## v0.5.0 (2026-05-27)
-
-### Feat
-
-- rename Loki to Coyote
-
-### Fix
-
-- bash-based user interactions in agents accidentally regressed in graph implementation
-- Claude function calling in agent contexts
-- Claude code rate limit error per new Claude changes
-
-## v0.4.0 (2026-05-23)
-
-### Feat
-
-- LLM node failures propgate up
-- Added .install remote tab completions to the REPL
-- feature complete install remote with category selection
-- Support to interactively add secrets to Coyote that are missing from MCP configs when merging
-- Added MCP config merging support for remote asset installations
-- install remote now writes files to disk
-- Created basic install_remote functions
-- Created a more comprehensive and immediately useful default config for first runs
-- Created an example graph-based agent called deep-research
-- Improved coder agent that is now a graph-based agent
-- Removed indicatif spinners. The UX just won't stop clobbering for parallel graph nodes
-- Added agent variables support for graph agents and improved script executor to use the same environment variables as normal agent tool calling for further flexibility
-- Improved UX with colored spinners for parallel graph agents and no clobbering outputs for sub-agents
-- created new graph-based deep-research agent
-- improved UX for parallel graph execution
-- added branch progress tracker for better visualization of parallel graph super-steps
-- Removed the jira-helper agent and replaced it with the atlassian role
-- created the RenderMode enum to suppress stdout streaming during parallel graph super-steps
-- Full support for map node types
-- implemented the frontier-based scheduling for the graph executor with simplified state management (gotta love .clone)
-- validation support for parallel graph execution; restricted map nodes to only run for nodes without next targets and not supporting chained map nodes
-- created the staging area for state merges per super-step and created the built-in reducers (and their application) for the state merge phase of a super step
-- scaffolding work for fan-out nodes for parallel branch execution support and stubbed out Map node types
-- Coyote can now update itself via .update and --update commands
-- added a .edit command for editing the MCP configuration file
-- Created a new .install command to install bundled assets on-demand
-- migrated llm node validation to graph loading time instead of graph runtime
-- ripped out user input timeout scaffolding for approval and input node types; implementation can't be done cleanly
-- added additional support for all RAG-configuration fields in RAG nodes
-- initial support for RAG nodes in the graph execution system
-- implemented structured logging for graph execution
-- merged normal agent config and graph agent configs into one file (either/or)
-- added structured-output extraction for llm and agent nodes
-- created full llm node runtime implementation
-- scaffolded together the initial llm node type and its executor
-- wired together graph execution and agent graph dispatch
-- implemented support for the graph executor
-- created the approval node executor and the input node executor for user interaction
-- Added initial support for native Coyote agent nodes in the graph-based agent system
-- Added direct script invocation support for graph-based agents
-- Added graph validation
-- Implemented state management for agent graphs
-- initial agent graph scaffolding
-- add auto-continue support to all contexts
-- dynamic tab completions now show the sessions for a given agent instead of only listing global sessions
-- legacy SSE support for MCP server configurations
-- support http/sse transport types for MCP server configurations so it fully supports claude desktop-style MCP configs
-- 99% complete migration to new state structs to get away from God-Config struct; i.e. AppConfig, AppState, and RequestContext
-- Automatic runtime customization using shebangs
-- Created a demo TypeScript tool and a get_current_weather function in TypeScript
-- Updated the Python demo tool to show all possible parameter types and variations
-- Added TypeScript tool support using the refactored common ScriptedLanguage trait
-
-### Fix
-
-- Generified the functions usage of script detection for an executable bit on unix systems
-- merge required claude code system prompt into instructions
-- updated argc argument passing in run-tool and run-agent scripts
-- Added additional graph validation for parallel reads and writes with dependencies between nodes states
-- bug in next_single method and improved outcome handling for LLM node execution
-- inline RAG bug when globbing files by extension without subdirectory globbing
-- update the estimate_token_length function to use the standard word count method
-- removed unnecessary regenerate logic for sessions and use the same logic for all contexts; prevents a panic on empty message list
-- error when users try to start a session on a graph agent
-- added on_other field for approval nodes so users can specify an alternative free-text target when none of the options match what they want
-- accidentally added back in full agent tools on LLM nodes
-- Improve the coder agent's usage of tools
-- make the agent__collect escalation-aware so it doesn't freeze on sub-agent escalations
-- check for an existing session before starting up MCP servers when switching to a role
-- do not switch to agent if a session is active.
-- Do not append todo instructions when function calling is disabled
-- a bug in the dynamic completions because the crate name is coyote-ai but the binary is named coyote
-- bug found by copilot that would create a lock on the PollSender for sse-based MCP servers
-- Accidental shadow of temp_file function for Windows function calling
-- upgraded to newer rmcp version to get native-tls support
-- RagCache was not being used for agent and sub-agent instantiation
-- TypeScript function args were being passed as objects rather than direct parameters
-- Added in forgotten wrapper scripts for TypeScript tools
-- don't shadow variables in binary path handling for Windows
-- Tool call improvements for Windows systems
-
-### Refactor
-
-- migrated llm nodes to use Roles to simplify instructions handling and to function like inline roles
-- migrated the next_node and apply_state_updates logic for LLM nodes into the LlmExecutor
-- fully complete state re-architecting
-- Fully ripped out the god Config struct
-- Deprecated old Config struct initialization logic
-- migrate functions and MCP servers to AppConfig
-- Migrate the vault/bare_init logic
-- created a single install_builtins free function to remove from Config::init
-- partial migration to init in AppConfig
-- Extracted common Python parser logic into a common.rs module
-- python tools now use tree-sitter queries instead of AST
-
 ## v0.3.0 (2026-04-02)
 
 ### Feat
@@ -269,7 +21,7 @@
 - Created a CodeRabbit-style code-reviewer agent
 - Added configuration option in agents to indicate the timeout for user input before proceeding (defaults to 5 minutes)
 - Added support for sub-agents to escalate user interaction requests from any depth to the parent agents for user interactions
-- built-in user interaction tools to remove the need for the list/confirm/etc prompts in prompt tools and to enhance user interactions in Coyote
+- built-in user interaction tools to remove the need for the list/confirm/etc prompts in prompt tools and to enhance user interactions in Loki
 - Experimental update to sisyphus to use the new parallel agent spawning system
 - Added an agent configuration property that allows auto-injecting sub-agent spawning instructions (when using the built-in sub-agent spawning system)
 - Auto-dispatch support of sub-agents and support for the teammate pattern between subagents
@@ -323,7 +75,7 @@
 
 - Simplified sisyphus prompt to improve functionality
 - Supported the injection of RAG sources into the prompt, not just via the `.sources rag` command in the REPL so models can directly reference the documents that supported their responses
-- Created the Sisyphus agent to make Coyote function like Claude Code, Gemini, Codex, etc.
+- Created the Sisyphus agent to make Loki function like Claude Code, Gemini, Codex, etc.
 - Created the Oracle agent to handle high-level architectural decisions and design questions about a given codebase
 - Updated the coder agent to be much more task-focused and to be delegated to by Sisyphus
 - Created the explore agent for exploring codebases to help answer questions
@@ -383,8 +135,8 @@
 - Support for secret injection into the global config file (API keys, for example)
 - Improved MCP handling toggle handling
 - Secret injection into the MCP configuration
-- added REPL support for interacting with the Coyote vault
+- added REPL support for interacting with the Loki vault
-- Integrated gman with Coyote to create a vault and added flags to configure the Coyote vault
+- Integrated gman with Loki to create a vault and added flags to configure the Loki vault
 - Added a default session to the jira helper to make interaction more natural
 - Created the repo-analyzer role
 - Created the coder and sql agents

CONTRIBUTING.md

@@ -2,7 +2,7 @@
 Contributors are very welcome! **No contribution is too small and all contributions are valued.**
 
 ## Rust
-You'll need to have the stable Rust toolchain installed in order to develop Coyote.
+You'll need to have the stable Rust toolchain installed in order to develop Loki.
 
 The Rust toolchain (stable) can be installed via rustup using the following command:
 
@@ -84,5 +84,5 @@ Claude, etc.) is not permitted unless explicitly disclosed and approved.
 Submissions must certify that the contributor understands and can maintain the code they submit.
 
 ## Questions? Reach out to me!
-If you encounter any questions while developing Coyote, please don't hesitate to reach out to me at 
+If you encounter any questions while developing Loki, please don't hesitate to reach out to me at 
 alex.j.tusa@gmail.com. I'm happy to help contributors in any way I can, regardless of if they're new or experienced!

CREDITS.md

@@ -1,19 +1,19 @@
 # Credits
 
 ## AIChat
-Coyote originally started as a fork of the fantastic
+Loki originally started as a fork of the fantastic
 [AIChat CLI](https://github.com/sigoden/aichat). The initial goal was simply
 to fix a bug in how MCP servers worked with AIChat, allowing different MCP
-servers to be specified per agent. Since then, Coyote has evolved far beyond
+servers to be specified per agent. Since then, Loki has evolved far beyond
 its original scope and grown into a passion project with a life of its own.
 
-Today, Coyote includes first-class MCP server support (for both local and remote
+Today, Loki includes first-class MCP server support (for both local and remote
 servers), a built-in vault for interpolating secrets in configuration files,
 built-in agents and macros, dynamic tab completions, integrated custom
 functions (no external `argc` dependency), improved documentation, and much
 more with many more ideas planned for the future.
 
-Coyote is now developed and maintained as an independent project. Full credit
+Loki is now developed and maintained as an independent project. Full credit
 for the original foundation goes to the developers of the wonderful
 AIChat project.
 
@@ -21,10 +21,10 @@ This project is not affiliated with or endorsed by the AIChat maintainers.
 
 ## AIChat
 
-Coyote originally began as a fork of [AIChat CLI](https://github.com/sigoden/aichat),
+Loki originally began as a fork of [AIChat CLI](https://github.com/sigoden/aichat),
 created and maintained by the AIChat contributors.
 
-While Coyote has since diverged significantly and is now developed as an
+While Loki has since diverged significantly and is now developed as an
 independent project, its early foundation and inspiration came from the
 AIChat project.
 

Cargo.toml

@@ -1,16 +1,16 @@
 [package]
-name = "coyote-ai"
+name = "loki-ai"
-version = "0.7.2"
+version = "0.3.0"
 edition = "2024"
 authors = ["Alex Clarke <alex.j.tusa@gmail.com>"]
 description = "An all-in-one, batteries included LLM CLI Tool"
 keywords = ["chatgpt", "llm", "cli", "ai", "repl"]
-homepage = "https://github.com/Dark-Alex-17/coyote"
+homepage = "https://github.com/Dark-Alex-17/loki"
-repository = "https://github.com/Dark-Alex-17/coyote"
+repository = "https://github.com/Dark-Alex-17/loki"
 categories = ["command-line-utilities"]
 readme = "README.md"
 license = "MIT"
-rust-version = "1.95.0"
+rust-version = "1.89.0"
 exclude = [".github", "CONTRIBUTING.md"]
 
 [dependencies]
@@ -22,7 +22,7 @@ dunce = "1.0.5"
 futures-util = "0.3.29"
 inquire = "0.9.4"
 is-terminal = "0.4.9"
-reedline = "0.47.0"
+reedline = "0.46.0"
 serde = { version = "1.0.152", features = ["derive"] }
 serde_json = { version = "1.0.93", features = ["preserve_order"] }
 serde_yaml = "0.9.17"
@@ -34,6 +34,10 @@ tokio = { version = "1.34.0", features = [
   "rt-multi-thread",
   "full",
 ] }
+tokio-graceful = "0.2.2"
+tokio-stream = { version = "0.1.15", default-features = false, features = [
+  "sync",
+] }
 crossterm = "0.29.0"
 chrono = "0.4.23"
 bincode = { version = "2.0.0", features = [
@@ -47,7 +51,7 @@ nu-ansi-term = "0.50.0"
 async-trait = "0.1.74"
 textwrap = "0.16.0"
 ansi_colours = "1.2.2"
-eventsource-stream = "0.2.3"
+reqwest-eventsource = "0.6.0"
 log = "0.4.28"
 log4rs = { version = "1.4.0", features = ["file_appender"] }
 shell-words = "1.1.0"
@@ -55,16 +59,20 @@ sha2 = "0.10.8"
 unicode-width = "0.2.0"
 async-recursion = "1.1.1"
 http = "1.1.0"
+http-body-util = "0.1"
+hyper = { version = "1.0", features = ["full"] }
+hyper-util = { version = "0.1", features = ["server-auto", "client-legacy"] }
+time = { version = "0.3.36", features = ["macros"] }
 indexmap = { version = "2.2.6", features = ["serde"] }
 hmac = "0.12.1"
 aws-smithy-eventstream = "0.60.4"
-aws-smithy-types = "=1.4.9"
-time = "=0.3.47"
 urlencoding = "2.1.3"
+unicode-segmentation = "1.11.0"
 json-patch = { version = "4.0.0", default-features = false }
 bitflags = "2.5.0"
 path-absolutize = "3.1.1"
 hnsw_rs = "0.3.0"
+rayon = "1.10.0"
 uuid = { version = "1.9.1", features = ["v4"] }
 scraper = { version = "0.23.1", default-features = false, features = [
   "deterministic",
@@ -89,33 +97,25 @@ rmcp = { version = "1.5.0", features = [
 ] }
 num_cpus = "1.17.0"
 tree-sitter = "0.26.8"
+tree-sitter-language = "0.1"
 tree-sitter-python = "0.25.0"
 tree-sitter-typescript = "0.23"
 colored = "3.0.0"
 clap_complete = { version = "4.5.58", features = ["unstable-dynamic"] }
-gman = "0.5.0"
+gman = "0.4.1"
 clap_complete_nushell = "4.5.9"
 open = "5"
 rand = { version = "0.10.0", features = ["default"] }
 url = "2.5.8"
-self_update = { version = "0.44", default-features = false, features = [
-  "reqwest",
-  "rustls",
-  "archive-tar",
-  "compression-flate2",
-  "archive-zip",
-  "compression-zip-deflate",
-] }
 
 [dependencies.reqwest]
-version = "0.13.3"
+version = "0.12.0"
 features = [
   "json",
   "multipart",
-  "stream",
-  "form",
   "socks",
-  "rustls",
+  "rustls-tls",
+  "rustls-tls-native-roots",
 ]
 default-features = false
 
@@ -140,7 +140,7 @@ pretty_assertions = "1.4.0"
 serial_test = "3"
 
 [[bin]]
-name = "coyote"
+name = "loki"
 path = "src/main.rs"
 
 [profile.release]

README.md

@@ -1,116 +1,121 @@
-# Coyote: All-in-one, batteries-included LLM CLI Tool
+# Loki: All-in-one, batteries-included LLM CLI Tool
 
-![Test](https://github.com/Dark-Alex-17/coyote/actions/workflows/ci.yaml/badge.svg)
+![Test](https://github.com/Dark-Alex-17/loki/actions/workflows/ci.yaml/badge.svg)
-[![crates.io link](https://img.shields.io/crates/v/coyote-ai.svg)](https://crates.io/crates/coyote-ai)
+![LOC](https://tokei.rs/b1/github/Dark-Alex-17/loki?category=code)
-![Release](https://img.shields.io/github/v/release/Dark-Alex-17/coyote?color=%23c694ff)
+[![crates.io link](https://img.shields.io/crates/v/loki-ai.svg)](https://crates.io/crates/loki-ai)
-![Crate.io downloads](https://img.shields.io/crates/d/coyote-ai?label=Crate%20downloads)
+![Release](https://img.shields.io/github/v/release/Dark-Alex-17/loki?color=%23c694ff)
-[![GitHub Downloads](https://img.shields.io/github/downloads/Dark-Alex-17/coyote/total.svg?label=GitHub%20downloads)](https://github.com/Dark-Alex-17/coyote/releases)
+![Crate.io downloads](https://img.shields.io/crates/d/loki-ai?label=Crate%20downloads)
+[![GitHub Downloads](https://img.shields.io/github/downloads/Dark-Alex-17/loki/total.svg?label=GitHub%20downloads)](https://github.com/Dark-Alex-17/loki/releases)
 
-Coyote is an all-in-one, batteries-included, LLM CLI tool featuring Shell Assistant, CLI & REPL Mode, RAG, AI Tools & 
+Loki is an all-in-one, batteries-included, LLM CLI tool featuring Shell Assistant, CLI & REPL Mode, RAG, AI Tools & 
 Agents, and More.
 
-It is designed to include a number of useful agents, roles, macros, and more so users can get up and running with Coyote 
+It is designed to include a number of useful agents, roles, macros, and more so users can get up and running with Loki 
-in as little time as possible. You can also install entire bundles of agents, roles, macros, tools, and MCP servers from 
+in as little time as possible.
-any git repository. See [Sharing Configurations](https://github.com/Dark-Alex-17/coyote/wiki/Sharing-Configurations) for more information.
 
-![Agent example](https://raw.githubusercontent.com/wiki/Dark-Alex-17/coyote/images/agents/sql.gif)
+![Agent example](./docs/images/agents/sql.gif)
 
-Coming from [AIChat](https://github.com/sigoden/aichat)? Follow the [migration guide](https://github.com/Dark-Alex-17/coyote/wiki/AIChat-Migration) to get started.
+Coming from [AIChat](https://github.com/sigoden/aichat)? Follow the [migration guide](./docs/AICHAT-MIGRATION.md) to get started.
 
 ## Quick Links
-* [AIChat Migration Guide](https://github.com/Dark-Alex-17/coyote/wiki/AIChat-Migration): Coming from AIChat? Follow the migration guide to get started.
+* [AIChat Migration Guide](./docs/AICHAT-MIGRATION.md): Coming from AIChat? Follow the migration guide to get started.
-* [Installation](#install): Install Coyote
+* [Installation](#install): Install Loki
-* [Getting Started](#getting-started): Get started with Coyote by doing first-run setup steps.
+* [Getting Started](#getting-started): Get started with Loki by doing first-run setup steps.
-* [Sharing Configurations](https://github.com/Dark-Alex-17/coyote/wiki/Sharing-Configurations): Install bundles of agents, roles, macros, tools, and MCP servers from any git repo, and share your own.
+* [REPL](./docs/REPL.md): Interactive Read-Eval-Print Loop for conversational interactions with LLMs and Loki.
-* [REPL](https://github.com/Dark-Alex-17/coyote/wiki/REPL): Interactive Read-Eval-Print Loop for conversational interactions with LLMs and Coyote.
+  * [Custom REPL Prompt](./docs/REPL-PROMPT.md): Customize the REPL prompt to provide useful contextual information.
-  * [Custom REPL Prompt](https://github.com/Dark-Alex-17/coyote/wiki/REPL-Prompt): Customize the REPL prompt to provide useful contextual information.
+* [Vault](./docs/VAULT.md): Securely store and manage sensitive information such as API keys and credentials.
-* [Vault](https://github.com/Dark-Alex-17/coyote/wiki/Vault): Securely store and manage sensitive information such as API keys and credentials.
+* [Shell Integrations](./docs/SHELL-INTEGRATIONS.md): Seamlessly integrate Loki with your shell environment for enhanced command-line assistance.
-* [Sandboxes](https://github.com/Dark-Alex-17/coyote/wiki/Sandboxes): Launch Coyote inside an isolated [Docker Sandbox](https://docs.docker.com/ai/sandboxes/) with one command. Host config and vault credentials are projected in automatically; everything else is delegated to the `sbx` CLI.
+* [Function Calling](./docs/function-calling/TOOLS.md#Tools): Leverage function calling capabilities to extend Loki's functionality with custom tools
-* [Shell Integrations](https://github.com/Dark-Alex-17/coyote/wiki/Shell-Integrations): Seamlessly integrate Coyote with your shell environment for enhanced command-line assistance.
+    * [Creating Custom Tools](./docs/function-calling/CUSTOM-TOOLS.md): You can create your own custom tools to enhance Loki's capabilities.
-* [Function Calling](https://github.com/Dark-Alex-17/coyote/wiki/Tools): Leverage function calling capabilities to extend Coyote's functionality with custom tools
+        * [Create Custom Python Tools](./docs/function-calling/CUSTOM-TOOLS.md#custom-python-based-tools)
-    * [Creating Custom Tools](https://github.com/Dark-Alex-17/coyote/wiki/Custom-Tools): You can create your own custom tools to enhance Coyote's capabilities.
+        * [Create Custom TypeScript Tools](./docs/function-calling/CUSTOM-TOOLS.md#custom-typescript-based-tools)
-        * [Create Custom Python Tools](https://github.com/Dark-Alex-17/coyote/wiki/Custom-Tools#custom-python-based-tools)
+        * [Create Custom Bash Tools](./docs/function-calling/CUSTOM-BASH-TOOLS.md)
-        * [Create Custom TypeScript Tools](https://github.com/Dark-Alex-17/coyote/wiki/Custom-Tools#custom-typescript-based-tools)
+            * [Bash Prompt Utilities](./docs/function-calling/BASH-PROMPT-HELPERS.md)
-        * [Create Custom Bash Tools](https://github.com/Dark-Alex-17/coyote/wiki/Custom-Bash-Tools)
+* [First-Class MCP Server Support](./docs/function-calling/MCP-SERVERS.md): Easily connect and interact with MCP servers for advanced functionality.
-            * [Bash Prompt Utilities](https://github.com/Dark-Alex-17/coyote/wiki/Bash-Prompt-Helpers)
+* [Macros](./docs/MACROS.md): Automate repetitive tasks and workflows with Loki "scripts" (macros).
-* [First-Class MCP Server Support](https://github.com/Dark-Alex-17/coyote/wiki/MCP-Servers): Easily connect and interact with MCP servers for advanced functionality.
+* [RAG](./docs/RAG.md): Retrieval-Augmented Generation for enhanced information retrieval and generation.
-* [Macros](https://github.com/Dark-Alex-17/coyote/wiki/Macros): Automate repetitive tasks and workflows with Coyote "scripts" (macros).
+* [Sessions](/docs/SESSIONS.md): Manage and persist conversational contexts and settings across multiple interactions.
-* [RAG](https://github.com/Dark-Alex-17/coyote/wiki/RAG): Retrieval-Augmented Generation for enhanced information retrieval and generation.
+* [Roles](./docs/ROLES.md): Customize model behavior for specific tasks or domains.
-* [Sessions](https://github.com/Dark-Alex-17/coyote/wiki/Sessions): Manage and persist conversational contexts and settings across multiple interactions.
+* [Agents](/docs/AGENTS.md): Leverage AI agents to perform complex tasks and workflows, including sub-agent spawning, teammate messaging, and user interaction tools.
-* [Memory](https://github.com/Dark-Alex-17/coyote/wiki/Memory): Persistent file-based memory that survives across sessions. Bootstrap with `coyote --init-memory [global|workspace]`.
+    * [Todo System](./docs/TODO-SYSTEM.md): Built-in task tracking for improved agent reliability with smaller models.
-* [Roles](https://github.com/Dark-Alex-17/coyote/wiki/Roles): Customize model behavior for specific tasks or domains.
+* [Environment Variables](./docs/ENVIRONMENT-VARIABLES.md): Override and customize your Loki configuration at runtime with environment variables.
-* [Skills](https://github.com/Dark-Alex-17/coyote/wiki/Skills): Modular knowledge or capability packs the LLM can load and unload mid-conversation. Multiple skills compose; instructions stack, tools and MCPs union.
+* [Client Configurations](./docs/clients/CLIENTS.md): Configuration instructions for various LLM providers.
-* [Agents](https://github.com/Dark-Alex-17/coyote/wiki/Agents): Leverage AI agents to perform complex tasks and workflows, including sub-agent spawning, teammate messaging, and user interaction tools.
+    * [Authentication (API Key & OAuth)](./docs/clients/CLIENTS.md#authentication): Authenticate with API keys or OAuth for subscription-based access.
-    * [Graph Agents](https://github.com/Dark-Alex-17/coyote/wiki/Graph-Agents): Define an agent as a declarative, YAML-driven workflow. A directed graph of typed nodes (LLM calls, scripts, approvals, user input, RAG retrieval, sub-agent spawns).
+    * [Patching API Requests](./docs/clients/PATCHES.md): Learn how to patch API requests for advanced customization.
-* [Todo System](https://github.com/Dark-Alex-17/coyote/wiki/TODO-System): Built-in task tracking for improved LLM reliability with smaller models.
+* [Custom Themes](./docs/THEMES.md): Change the look and feel of Loki to your preferences with custom themes.
-* [Environment Variables](https://github.com/Dark-Alex-17/coyote/wiki/Environment-Variables): Override and customize your Coyote configuration at runtime with environment variables.
+* [History](#history): A history of how Loki came to be.
-* [Client Configurations](https://github.com/Dark-Alex-17/coyote/wiki/Clients): Configuration instructions for various LLM providers.
-    * [Authentication (API Key & OAuth)](https://github.com/Dark-Alex-17/coyote/wiki/Clients#authentication): Authenticate with API keys or OAuth for subscription-based access.
-    * [Patching API Requests](https://github.com/Dark-Alex-17/coyote/wiki/Patches): Learn how to patch API requests for advanced customization.
-* [Custom Themes](https://github.com/Dark-Alex-17/coyote/wiki/Themes): Change the look and feel of Coyote to your preferences with custom themes.
-* [History](#history): A history of how Coyote came to be.
 
 ## Prerequisites
-Coyote requires the following tools to be installed on your system:
+Loki requires the following tools to be installed on your system:
 * [jq](https://github.com/jqlang/jq)
     * `brew install jq`
+* [jira (optional)](https://github.com/ankitpokhrel/jira-cli/wiki/Installation) (For the `query_jira_issues` tool)
+    * `brew tap ankitpokhrel/jira-cli && brew install jira-cli`
+    * You'll need to [create a JIRA API token](https://id.atlassian.com/manage-profile/security/api-tokens) for authentication
+    * Then, save it as an environment variable to your shell profile:
+      ```sh
+      # ~/.bashrc or ~/.zshrc
+      export JIRA_API_TOKEN="your_jira_api_token_here"
+      ```
+    * Then run `jira init`, select installation type as `cloud`, and provide the required details to generate a config
+      file for the Jira CLI.
 * [usql](https://github.com/xo/usql) (For the `sql` agent)
     * `brew install xo/xo/usql`
 * [docker](https://docs.docker.com/engine/install/)
 * [uv](https://docs.astral.sh/uv/getting-started/installation/)
     * `curl -LsSf https://astral.sh/uv/install.sh | sh`
 
-These tools are used to provide various functionalities within Coyote, such as document processing, JSON manipulation,
+These tools are used to provide various functionalities within Loki, such as document processing, JSON manipulation,
-etc., and they are used within agents and tools.
+interaction with Jira, and they are used within agents and tools.
 
 ## Install
 
 ### Cargo
-If you have Cargo installed, then you can install `coyote` from Crates.io:
+If you have Cargo installed, then you can install `loki` from Crates.io:
 
 ```shell
-cargo install coyote-ai # Binary name is `coyote`
+cargo install loki-ai # Binary name is `loki`
 
 # If you encounter issues installing, try installing with '--locked'
-cargo install --locked coyote-ai
+cargo install --locked loki-ai
 ```
 
 ### Homebrew (Mac/Linux)
-To install Coyote from Homebrew, install the `coyote` tap. Then you'll be able to install `coyote`:
+To install Loki from Homebrew, install the `loki` tap. Then you'll be able to install `loki`:
 
 ```shell
-brew tap Dark-Alex-17/coyote
+brew tap Dark-Alex-17/loki
-brew install coyote
+brew install loki
 
 # If you need to be more specific, use:
-brew install Dark-Alex-17/coyote/coyote
+brew install Dark-Alex-17/loki/loki
 ```
 
-To upgrade `coyote` using Homebrew:
+To upgrade `loki` using Homebrew:
 
 ```shell
-brew upgrade coyote
+brew upgrade loki
 ```
 
 ### Scripts
 #### Linux/MacOS (`bash`)
-You can use the following command to run a bash script that downloads and installs the latest version of `coyote` for your
+You can use the following command to run a bash script that downloads and installs the latest version of `loki` for your
 OS (Linux/MacOS) and architecture (x86_64/arm64):
 
 ```shell
-curl -fsSL https://raw.githubusercontent.com/Dark-Alex-17/coyote/main/install_coyote.sh | bash
+curl -fsSL https://raw.githubusercontent.com/Dark-Alex-17/loki/main/install_loki.sh | bash
 ```
 
 #### Windows/Linux/MacOS (`PowerShell`)
-You can use the following command to run a PowerShell script that downloads and installs the latest version of `coyote`
+You can use the following command to run a PowerShell script that downloads and installs the latest version of `loki`
 for your OS (Windows/Linux/MacOS) and architecture (x86_64/arm64):
 
 ```powershell
-powershell -NoProfile -ExecutionPolicy Bypass -Command "iwr -useb https://raw.githubusercontent.com/Dark-Alex-17/coyote/main/scripts/install_coyote.ps1 | iex"
+powershell -NoProfile -ExecutionPolicy Bypass -Command "iwr -useb https://raw.githubusercontent.com/Dark-Alex-17/loki/main/scripts/install_loki.ps1 | iex"
 ```
 
 ### Manual
-Binaries are available on the [releases](https://github.com/Dark-Alex-17/coyote/releases) page for the following platforms:
+Binaries are available on the [releases](https://github.com/Dark-Alex-17/loki/releases) page for the following platforms:
 
 | Platform       | Architecture(s) |
 |----------------|-----------------|
@@ -121,58 +126,35 @@ Binaries are available on the [releases](https://github.com/Dark-Alex-17/coyote/
 #### Windows Instructions
 To use a binary from the releases page on Windows, do the following:
 
-1. Download the latest [binary](https://github.com/Dark-Alex-17/coyote/releases) for your OS.
+1. Download the latest [binary](https://github.com/Dark-Alex-17/loki/releases) for your OS.
 2. Use 7-Zip or TarTool to unpack the Tar file.
-3. Run the executable `coyote.exe`!
+3. Run the executable `loki.exe`!
 
 #### Linux/MacOS Instructions
 To use a binary from the releases page on Linux/MacOS, do the following:
 
-1. Download the latest [binary](https://github.com/Dark-Alex-17/coyote/releases) for your OS.
+1. Download the latest [binary](https://github.com/Dark-Alex-17/loki/releases) for your OS.
 2. `cd` to the directory where you downloaded the binary.
-3. Extract the binary with `tar -C /usr/local/bin -xzf coyote-<arch>.tar.gz` (Note: This may require `sudo`)
+3. Extract the binary with `tar -C /usr/local/bin -xzf loki-<arch>.tar.gz` (Note: This may require `sudo`)
-4. Now you can run `coyote`!
+4. Now you can run `loki`!
-
-## Updating
-Coyote can update itself in place to the latest GitHub release. Run `coyote --update`
-for the newest release, or `coyote --update v0.4.0` for a specific version:
-
-```shell
-coyote --update
-coyote --update v0.4.0
-```
-
-The same is available from within the REPL via `.update` and `.update v0.4.0`.
-
-If Coyote was installed with a package manager, prefer that package manager so its
-records stay in sync with the binary on disk; i.e. `brew upgrade coyote` for Homebrew,
-or `cargo install --locked coyote-ai` for Cargo.
-
-When Coyote detects a package-manager install it prints a warning and asks for
-confirmation. In a non-interactive shell (no TTY), pass `--force` to update
-anyway:
-
-```shell
-coyote --update --force
-```
 
 ## Getting Started
 After installation, you can generate the configuration files and directories by simply running:
 
 ```sh
-coyote --info
+loki --info
 ```
 
-Then, you need to set up the Coyote vault by creating a vault password file. Coyote will do this for you automatically and
+Then, you need to set up the Loki vault by creating a vault password file. Loki will do this for you automatically and
 guide you through the process when you first attempt to access the vault. So, to get started, you can run:
 
 ```sh
-coyote --list-secrets
+loki --list-secrets
 ```
 
 ### Authentication
 Each client in your configuration needs authentication (with a few exceptions; e.g. ollama). Most clients use an API key
-(set via `api_key` in the config or through the [vault](https://github.com/Dark-Alex-17/coyote/wiki/Vault)). For providers that support OAuth (e.g. Claude Pro/Max 
+(set via `api_key` in the config or through the [vault](./docs/VAULT.md)). For providers that support OAuth (e.g. Claude Pro/Max 
 subscribers, Google Gemini), you can authenticate with your existing subscription instead:
 
 ```yaml
@@ -184,40 +166,40 @@ clients:
 ```
 
 ```sh
-coyote --authenticate my-claude-oauth
+loki --authenticate my-claude-oauth
 # Or via the REPL: .authenticate
 ```
 
-For full details, see the [authentication documentation](https://github.com/Dark-Alex-17/coyote/wiki/Clients#authentication).
+For full details, see the [authentication documentation](./docs/clients/CLIENTS.md#authentication).
 
 ### Tab-Completions
-You can also enable tab completions to make using Coyote easier. To do so, add the following to your shell profile:
+You can also enable tab completions to make using Loki easier. To do so, add the following to your shell profile:
 ```shell
 # Bash
 # (add to: `~/.bashrc`)
-source <(COMPLETE=bash coyote) 
+source <(COMPLETE=bash loki) 
 
 # Zsh
 # (add to: `~/.zshrc`)
-source <(COMPLETE=zsh coyote)
+source <(COMPLETE=zsh loki)
 
 # Fish
 # (add to: `~/.config/fish/config.fish`)
-source <(COMPLETE=fish coyote | psub)
+source <(COMPLETE=fish loki | psub)
 
 # Elvish
 # (add to: `~/.elvish/rc.elv`)
-eval (E:COMPLETE=elvish coyote | slurp)
+eval (E:COMPLETE=elvish loki | slurp)
 
 # PowerShell
 # (add to: `$PROFILE`)
 $env:COMPLETE = "powershell"
-coyote | Out-String | Invoke-Expression
+loki | Out-String | Invoke-Expression
 ```
 
 ### Shell Integration
-You can integrate Coyote's Shell Assistant into your shell for enhanced command-line assistance. Add the code in the
+You can integrate Loki's Shell Assistant into your shell for enhanced command-line assistance. Add the code in the
-corresponding [shell integration script](./scripts/shell-integration) to your shell. Then, you can invoke Coyote to convert natural language to 
+corresponding [shell integration script](./scripts/shell-integration) to your shell. Then, you can invoke Loki to convert natural language to 
 shell commands by pressing `Alt-e`. For example:
 
 ```shell
@@ -227,18 +209,18 @@ find . -name "*.md"
 ```
 
 ## Configuration
-The location of the global Coyote configuration varies between systems, so you can use the following command to find your
+The location of the global Loki configuration varies between systems, so you can use the following command to find your
 `config.yaml` file:
 
 ```shell
-coyote --info | grep 'config_file' | awk '{print $2}'
+loki --info | grep 'config_file' | awk '{print $2}'
 ```
 
 The configuration file consists of a number of settings. To see a full example configuration file with every setting
 defined, refer to the [example configuration file](./config.example.yaml).
 
 ### Default LLM
-The following settings are available to configure the default LLM that is used when you start Coyote, and its
+The following settings are available to configure the default LLM that is used when you start Loki, and its
 hyperparameters:
 
 | Setting       | Description                                                                                                                                             |
@@ -248,34 +230,34 @@ hyperparameters:
 | `top_p`       | The default `top_p` hyperparameter value to use for all models, with a range of (0,1) (or (0,2) for some models); <br>Used unless explicitly overridden |
 
 ### CLI Behavior
-You can use the following settings to modify the behavior of Coyote:
+You can use the following settings to modify the behavior of Loki:
 
 | Setting       | Default Value | Description                                                                                                                         |
 |---------------|---------------|-------------------------------------------------------------------------------------------------------------------------------------|
 | `stream`      | `true`        | Controls whether to use stream-style APIs when querying for completions from LLM providers                                          |
 | `save`        | `true`        | Controls whether to save each query/response to every model to `messages.md` for posterity; Useful for debugging                    |
 | `keybindings` | `emacs`       | Specifies which keybinding schema to use; can either be `emacs` or `vi`                                                             |
-| `editor`      | `null`        | What text editor Coyote should use to edit the input buffer or session (e.g. `vim`, `emacs`, `nano`, `hx`); <br>Defaults to `$EDITOR` |
+| `editor`      | `null`        | What text editor Loki should use to edit the input buffer or session (e.g. `vim`, `emacs`, `nano`, `hx`); <br>Defaults to `$EDITOR` |
 | `wrap`        | `no`          | Controls whether text is wrapped (can be `no`, `auto`, or some `<max_width>`                                                        |
 | `wrap_code`   | `false`       | Enables or disables the wrapping of code blocks                                                                                     |
 
 ### Preludes
-Preludes let you define the default behavior for the different operating modes of Coyote. The available settings are
+Preludes let you define the default behavior for the different operating modes of Loki. The available settings are
 shown below:
 
 | Setting         | Description                                                                                                                                                                                                                                                                                                 |
 |-----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| `repl_prelude`  | This setting lets you specify a default `session` or `role` to use when starting Coyote in [REPL](https://github.com/Dark-Alex-17/coyote/wiki/REPL) mode. <br>Values can be <ul><li>`role:<name>` to define a role</li><li>`session:<name>` to define a session</li><li>`<session>:<role>` to define both a session and a role to use</li></ul> |
+| `repl_prelude`  | This setting lets you specify a default `session` or `role` to use when starting Loki in [REPL](./docs/REPL.md) mode. <br>Values can be <ul><li>`role:<name>` to define a role</li><li>`session:<name>` to define a session</li><li>`<session>:<role>` to define both a session and a role to use</li></ul> |
-| `cmd_prelude`   | This setting lets you specify a default `session` or `role` to use when running one-off queries in Coyote via the CLI. <br>Values can be <ul><li>`role:<name>` to define a role</li><li>`session:<name>` to define a session</li><li>`<session>:<role>` to define both a session and a role to use</li></ul>  |
+| `cmd_prelude`   | This setting lets you specify a default `session` or `role` to use when running one-off queries in Loki via the CLI. <br>Values can be <ul><li>`role:<name>` to define a role</li><li>`session:<name>` to define a session</li><li>`<session>:<role>` to define both a session and a role to use</li></ul>  |
 | `agent_session` | This setting is used to specify a default session that all agents should start into, unless otherwise specified in the agent configuration. (e.g. `temp`, `default`)                                                                                                                                        |
 
 ### Appearance
-The appearance of Coyote can be modified using the following settings:
+The appearance of Loki can be modified using the following settings:
 
 | Setting       | Default Value | Description                                          |
 |---------------|---------------|------------------------------------------------------|
 | `highlight`   | `true`        | This setting enables or disables syntax highlighting |
-| `light_theme` | `false`       | This setting toggles light mode in Coyote              |
+| `light_theme` | `false`       | This setting toggles light mode in Loki              |
 
 ### Miscellaneous Settings
 | Setting              | Default Value | Description                                                                                                      |
@@ -287,7 +269,7 @@ The appearance of Coyote can be modified using the following settings:
 
 ## History
 
-Coyote began as a fork of [AIChat CLI](https://github.com/sigoden/aichat) and has since evolved into an independent project.
+Loki began as a fork of [AIChat CLI](https://github.com/sigoden/aichat) and has since evolved into an independent project.
 
 See [CREDITS.md](./CREDITS.md) for full attribution and background.
 

assets/agents/.shared/utils.sh

@@ -7,14 +7,14 @@ set -euo pipefail
 #######################
 
 # Cache file name for detected project info
-_COYOTE_PROJECT_CACHE=".coyote-project.json"
+_LOKI_PROJECT_CACHE=".loki-project.json"
 
 # Read cached project detection if valid
 # Usage: _read_project_cache "/path/to/project"
 # Returns: cached JSON on stdout (exit 0) or nothing (exit 1)
 _read_project_cache() {
   local dir="$1"
-  local cache_file="${dir}/${_COYOTE_PROJECT_CACHE}"
+  local cache_file="${dir}/${_LOKI_PROJECT_CACHE}"
 
   if [[ -f "${cache_file}" ]]; then
     local cached
@@ -32,7 +32,7 @@ _read_project_cache() {
 _write_project_cache() {
   local dir="$1"
   local json="$2"
-  local cache_file="${dir}/${_COYOTE_PROJECT_CACHE}"
+  local cache_file="${dir}/${_LOKI_PROJECT_CACHE}"
 
   echo "${json}" > "${cache_file}" 2>/dev/null || true
 }
@@ -238,7 +238,7 @@ _detect_with_llm() {
 	)
 
   local llm_response
-  llm_response=$(coyote --no-stream "${prompt}" 2>/dev/null) || return 1
+  llm_response=$(loki --no-stream "${prompt}" 2>/dev/null) || return 1
 
   llm_response=$(echo "${llm_response}" | sed 's/^```json//;s/^```//;s/```$//' | tr -d '\n' | sed 's/^[[:space:]]*//')
   llm_response=$(echo "${llm_response}" | grep -o '{[^}]*}' | head -1)

assets/agents/code-reviewer/config.yaml

@@ -1,6 +1,7 @@
 name: code-reviewer
 description: CodeRabbit-style code reviewer - spawns per-file reviewers, synthesizes findings
-version: 2.0.0
+version: 1.0.0
+temperature: 0.1
 
 auto_continue: true
 max_auto_continues: 20
@@ -10,11 +11,6 @@ can_spawn_agents: true
 max_concurrent_agents: 10
 max_agent_depth: 2
 
-skills_enabled: true
-enabled_skills:
-  - delegation-protocol
-  - parallel-research
-
 variables:
   - name: project_dir
     description: Project directory to review
@@ -22,7 +18,6 @@ variables:
 
 global_tools:
   - fs_read.sh
-  - fs_cat.sh
   - fs_grep.sh
   - fs_glob.sh
   - execute_command.sh
@@ -30,61 +25,31 @@ global_tools:
 instructions: |
   You are a code review orchestrator, similar to CodeRabbit. You coordinate per-file reviews and produce a unified report.
 
-  ## Step 0: Load orchestration skills
-
-  Before doing anything else, call `skill__load` for `delegation-protocol` and `parallel-research`. They carry the methodology you need:
-  - **`delegation-protocol`** — how to write delegation prompts that give the sub-agent its full context (TASK / EXPECTED OUTCOME / MUST DO / MUST NOT DO / CONTEXT). Apply this format when spawning each file-reviewer.
-  - **`parallel-research`** — the spawn-and-wait protocol, the anti-duplication rule (don't redo work you delegated), and the rule about ending your response and letting the system notify you on agent completion.
-
-  Both skills are always-on for this agent's workflow. Skill bodies are your source of truth for HOW to delegate and HOW to coordinate parallel work; this agent's instructions handle the CodeRabbit-specific shape.
-
   ## Workflow
 
   1. **Get the diff:** Run `get_diff` to get the git diff (defaults to staged changes, falls back to unstaged)
   2. **Parse changed files:** Extract the list of files from the diff
   3. **Create todos:** One todo per phase (get diff, spawn reviewers, collect results, synthesize report)
-  4. **Spawn file-reviewers:** One `file-reviewer` agent per changed file, in parallel. Apply the `delegation-protocol` structured prompt format.
+  4. **Spawn file-reviewers:** One `file-reviewer` agent per changed file, in parallel
   5. **Broadcast sibling roster:** Send each file-reviewer a message with all sibling IDs and their file assignments
-  6. **Collect all results:** Per `parallel-research`, do not poll. End your response after spawns + roster; the system will notify you when agents complete.
+  6. **Collect all results:** Wait for each file-reviewer to complete
   7. **Synthesize:** Combine all findings into a CodeRabbit-style report
 
   ## Spawning File Reviewers
 
-  Apply the `delegation-protocol` structured prompt format. Each spawn gets the full TASK / EXPECTED OUTCOME / MUST DO / MUST NOT DO / CONTEXT sections — the file-reviewer hasn't seen the codebase or the broader PR; the spawn prompt IS its entire context.
+  For each changed file, spawn a file-reviewer with a prompt containing:
+  - The file path
+  - The relevant diff hunk(s) for that file
+  - Instructions to review it
 
   ```
-  agent__spawn --agent file-reviewer --prompt "
+  agent__spawn --agent file-reviewer --prompt "Review the following diff for <file_path>:
-  ## TASK
-  Review the git diff for <file_path>. Produce structured findings per your output format.
 
-  ## EXPECTED OUTCOME
-  A REVIEW_COMPLETE-terminated report following your standard format:
-  - ## File: <file_path>
-  - ### Summary (1-2 sentences)
-  - ### Findings (each with severity, lines, description, suggestion)
-  - ### Cross-File Concerns (or 'None')
-
-  ## MUST DO
-  - Load `code-review` and `ai-slop-remover` skills before reading any code
-  - Apply both skill checklists to the diff
-  - Use targeted fs_read with offset/limit; max 5 file reads
-  - End with REVIEW_COMPLETE
-
-  ## MUST NOT DO
-  - Do not modify files (you are read-only)
-  - Do not review unchanged code unrelated to the diff
-  - Do not omit findings to keep the report short
-
-  ## CONTEXT
-  Project: {{project_dir}}
-  File under review: <file_path>
-
-  Diff:
   <diff content for this file>
-  "
-  ```
 
-  Paste the actual diff hunk(s) inline — the reviewer can't see your context. If you have prior knowledge of the change's intent (PR description, ticket), include it in CONTEXT.
+  Focus on bugs, security issues, logic errors, and style. Use the severity format (🔴🟡🟢💡).
+  End with REVIEW_COMPLETE."
+  ```
 
   ## Sibling Roster Broadcast
 
@@ -152,7 +117,6 @@ instructions: |
   3. **Don't review code yourself:** Delegate ALL review work to file-reviewers
   4. **Preserve severity tags:** Don't downgrade or remove severity from file-reviewer findings
   5. **Include ALL findings:** Don't summarize away specific issues
-  6. **File reads:** If you do read a file directly (e.g. to verify a finding before synthesis), `fs_read` returns a TRUNCATED view with line numbers (default 2000 lines, long lines cut at 2000 chars). Use `fs_cat` only when you need the FULL untruncated contents of a file.
 
   ## Context
   - Project: {{project_dir}}

assets/agents/coder/README.md

@@ -1,82 +1,40 @@
 # Coder
 
-A graph-based implementation agent. Plans, implements, and runs build +
+An AI agent that assists you with your coding tasks.
-tests in a bounded fix-loop until verified. Designed to be delegated to by
-the **[Sisyphus](../sisyphus/README.md)** agent.
 
-Coder is a [graph agent](https://github.com/Dark-Alex-17/coyote/wiki/Graph-Agents): its workflow is
+This agent is designed to be delegated to by the **[Sisyphus](../sisyphus/README.md)** agent to implement code specifications. Sisyphus
-defined declaratively in `graph.yaml`, with verification and the
+acts as the coordinator/architect, while Coder handles the implementation details.
-implement-fix loop enforced as graph edges rather than prose.
 
-## Workflow
+## Features
 
-```
+- 🏗️ Intelligent project structure creation and management
-analyze_request (llm + output_schema)   plan + complexity extraction
+- 🖼️ Convert screenshots into clean, functional code
-        ↓
+- 📁 Comprehensive file system operations (create folders, files, read/write files)
-route_complexity (script)               opt-out approval gate (complexity ≥ 7)
+- 🧐 Advanced code analysis and improvement suggestions
-        ↓
+- 📊 Precise diff-based file editing for controlled code modifications
-gate_approval (approval, optional)
-        ↓
-implement (llm + fs tools)              actual file edits
-        ↓
-verify_build (script)
-        ↓
-verify_tests (script)
-        ↓
-fix_loop_gate (script)                  back-edge to implement (bounded)
-        ↓
-end_success / end_rejected / end_failure
-```
 
-End nodes emit one of three sentinel outcomes for the caller:
+It can also be used as a standalone tool for direct coding assistance.
 
-- `CODER_COMPLETE` — build and tests passed.
+## Pro-Tip: Use an IDE MCP Server for Improved Performance
-- `CODER_REJECTED` — user rejected the plan at the approval gate.
+Many modern IDEs now include MCP servers that let LLMs perform operations within the IDE itself and use IDE tools. Using
-- `CODER_FAILED` — fix-loop exhausted; build/tests still failing.
+an IDE's MCP server dramatically improves the performance of coding agents. So if you have an IDE, try adding that MCP
-
+server to your config (see the [MCP Server docs](../../../docs/function-calling/MCP-SERVERS.md) to see how to configure
-## Tuning
+them), and modify the agent definition to look like this:
-
-The agent's `project_dir` is exposed via the standard `variables:` block,
-so it accepts the runtime override flag:
-
-```sh
-# Invoke from inside the project (project_dir defaults to ".")
-cd /path/to/your/project
-coyote -a coder "Add a foo() function..."
-
-# Or invoke from anywhere with an explicit override
-coyote -a coder --agent-variable project_dir /path/to/your/project "Add..."
-```
-
-`graph.yaml` `initial_state` exposes:
-
-- `max_fix_attempts` (default `3`) — fix-loop budget before `end_failure`.
-
-Environment overrides honored by the script nodes:
-
-- `BUILD_CMD` — skip project-type detection for the build/check command.
-- `TEST_CMD` — skip detection for tests.
-- `CODER_AUTOAPPROVE=1` — bypass the approval gate (for non-interactive runs
-  where complexity might trip the gate).
-
-## Pro-Tip: IDE MCP Server
-
-Modern IDEs (JetBrains, VS Code, Cursor, Zed, etc.) expose MCP servers
-that let LLMs use IDE tools directly. To wire one in, edit `graph.yaml`:
 
 ```yaml
+# ...
+
 mcp_servers:
-  - your-ide-mcp-server
+  - jetbrains # The name of your configured IDE MCP server
 
 global_tools:
-  # Keep read-only fs tools for files outside the IDE project
+  # Keep useful read-only tools for reading files in other non-project directories
   - fs_read.sh
   - fs_grep.sh
   - fs_glob.sh
 #  - fs_write.sh
 #  - fs_patch.sh
   - execute_command.sh
-```
 
-Then add the MCP server's write/patch tools to the `implement` node's
+# ...
-`tools:` whitelist.
+```

assets/agents/coder/config.yaml

@@ -0,0 +1,129 @@
+name: coder
+description: Implementation agent - writes code, follows patterns, verifies with builds
+version: 1.0.0
+temperature: 0.1
+
+auto_continue: true
+max_auto_continues: 15
+inject_todo_instructions: true
+
+variables:
+  - name: project_dir
+    description: Project directory to work in
+    default: '.'
+  - name: auto_confirm
+    description: Auto-confirm command execution
+    default: '1'
+
+global_tools:
+  - fs_read.sh
+  - fs_grep.sh
+  - fs_glob.sh
+  - fs_write.sh
+  - fs_patch.sh
+  - execute_command.sh
+
+instructions: |
+  You are a senior engineer. You write code that works on the first try.
+
+  ## Your Mission
+
+  Given an implementation task:
+  1. Check for orchestrator context first (see below)
+  2. Fill gaps only. Read files NOT already covered in context
+  3. Write the code (using tools, NOT chat output)
+  4. Verify it compiles/builds
+  5. Signal completion with a summary
+
+  ## Using Orchestrator Context (IMPORTANT)
+
+  When spawned by sisyphus, your prompt will often contain a `<context>` block
+  with prior findings: file paths, code patterns, and conventions discovered by
+  explore agents.
+
+  **If context is provided:**
+  1. Use it as your primary reference. Don't re-read files already summarized
+  2. Follow the code patterns shown. Snippets in context ARE the style guide
+  3. Read the referenced files ONLY IF you need more detail (e.g. full function
+     signature, import list, or adjacent code not included in the snippet)
+  4. If context includes a "Conventions" section, follow it exactly
+
+  **If context is NOT provided or is too vague to act on:**
+  Fall back to self-exploration: grep for similar files, read 1-2 examples,
+  match their style.
+
+  **Never ignore provided context.** It represents work already done upstream.
+
+  ## Todo System
+
+  For multi-file changes:
+  1. `todo__init` with the implementation goal
+  2. `todo__add` for each file to create/modify
+  3. Implement each, calling `todo__done` immediately after
+
+  ## Writing Code
+
+  **CRITICAL**: Write code using `write_file` tool, NEVER paste code in chat.
+
+  Correct:
+  ```
+  write_file --path "src/user.rs" --content "pub struct User { ... }"
+  ```
+
+  Wrong:
+  ```
+  Here's the implementation:
+  \`\`\`rust
+  pub struct User { ... }
+  \`\`\`
+  ```
+
+  ## File Reading Strategy (IMPORTANT - minimize token usage)
+
+  1. **Use grep to find relevant code** - `fs_grep --pattern "fn handle_request" --include "*.rs"` finds where things are
+  2. **Read only what you need** - `fs_read --path "src/main.rs" --offset 50 --limit 30` reads lines 50-79
+  3. **Never cat entire large files** - If 500+ lines, read the relevant section after grepping for it
+  4. **Use glob to find files** - `fs_glob --pattern "*.rs" --path src/` discovers files by name
+
+  ## Pattern Matching
+
+  Before writing ANY file:
+  1. Find a similar existing file (use `fs_grep` to locate, then `fs_read` to examine)
+  2. Match its style: imports, naming, structure
+  3. Follow the same patterns exactly
+
+  ## Verification
+
+  After writing files:
+  1. Run `verify_build` to check compilation
+  2. If it fails, fix the error (minimal change)
+  3. Don't move on until build passes
+
+  ## Completion Signal
+
+  When done, end your response with a summary so the parent agent knows what happened:
+
+  ```
+  CODER_COMPLETE: [summary of what was implemented, which files were created/modified, and build status]
+  ```
+
+  Or if something went wrong:
+  ```
+  CODER_FAILED: [what went wrong]
+  ```
+
+  ## Rules
+
+  1. **Write code via tools** - Never output code to chat
+  2. **Follow patterns** - Read existing files first
+  3. **Verify builds** - Don't finish without checking
+  4. **Minimal fixes** - If build fails, fix precisely
+  5. **No refactoring** - Only implement what's asked
+
+  ## Context
+  - Project: {{project_dir}}
+  - CWD: {{__cwd__}}
+  - Shell: {{__shell__}}
+  
+  ## Available tools:
+  {{__tools__}}

assets/agents/coder/graph.yaml

@@ -1,375 +0,0 @@
-name: coder
-description: |
-  Implementation agent. Plans, implements, and runs build + tests in a
-  bounded fix-loop until verified. Designed to be delegated to by sisyphus.
-version: "1.0"
-
-global_tools:
-  - fs_cat.sh
-  - fs_ls.sh
-  - fs_write.sh
-  - fs_patch.sh
-  - execute_command.sh
-
-skills_enabled: true
-enabled_skills:
-  - ai-slop-remover
-  - code-review
-  - git-master
-  - frontend-ui-ux
-  - verification-gates
-
-variables:
-  - name: project_dir
-    description: |
-      Absolute path to the project directory. Defaults to "." which is the
-      directory you invoked `coyote` from. Override at runtime with
-      `coyote -a coder --agent-variable project_dir /abs/path "..."`.
-    default: "."
-
-settings:
-  max_loop_iterations: 20
-  log_state_snapshots: true
-  validate_before_run: true
-  timeout: 1800
-
-initial_state:
-  project_dir: ""
-  fix_attempts: 0
-  max_fix_attempts: 3
-  fix_instructions: ""
-  build_output: ""
-  tests_output: ""
-  last_node_output: ""
-  plan_summary: ""
-  files_to_modify: []
-  files_to_create: []
-  risks: []
-  complexity_score: 0
-  review_attempts: 0
-  max_review_attempts: 1
-  review_clean: true
-  review_notes: ""
-
-start: resolve_paths
-
-nodes:
-  resolve_paths:
-    id: resolve_paths
-    type: script
-    description: Resolve project_dir to an absolute path from the agent variable
-    script: scripts/resolve_paths.sh
-    timeout: 5
-    fallback: end_failure
-
-  analyze_request:
-    id: analyze_request
-    type: llm
-    description: Extract a structured plan and complexity score from the orchestrator's prompt
-    instructions: |
-      You are a senior engineer's planning assistant. Read the orchestrator's
-      request and emit a structured plan. You only plan. You never edit files.
-
-      Score complexity from 1 to 10:
-        1-3: trivial - single file, <=20 lines changed, obvious approach
-        4-6: moderate - 2-5 files, clear approach, some pattern matching
-        7-10: complex - multi-component, ambiguous tradeoffs, refactoring,
-              or wide blast radius
-
-      Be specific in `files_to_modify` and `files_to_create`. All paths
-      MUST be absolute. The project root is {{project_dir}}. Prefer paths
-      like "{{project_dir}}/src/foo.rs" over "src/foo.rs". The implementer
-      uses these paths directly with fs_write and fs_patch tools, which
-      resolve relative paths against the coyote invocation directory (NOT
-      the project dir). Empty arrays are fine if no files in that category.
-
-      `risks` is a list of short strings. Anything that could derail the
-      implementation: unknown dependencies, brittle tests, blast radius,
-      etc. Empty list is fine.
-
-      Project directory: {{project_dir}}
-    prompt: "{{initial_prompt}}"
-    tools: []
-    output_schema:
-      type: object
-      properties:
-        plan_summary:
-          type: string
-          description: 1-3 sentences summarizing what will be done
-        files_to_modify:
-          type: array
-          items: {type: string}
-        files_to_create:
-          type: array
-          items: {type: string}
-        complexity_score:
-          type: integer
-          minimum: 1
-          maximum: 10
-        risks:
-          type: array
-          items: {type: string}
-      required: [plan_summary, files_to_modify, files_to_create, complexity_score, risks]
-    state_updates:
-      last_node_output: "{{output}}"
-    fallback: end_failure
-    next: route_complexity
-
-  route_complexity:
-    id: route_complexity
-    type: script
-    description: Route to approval gate for complex plans; skip otherwise
-    script: scripts/route_complexity.sh
-    timeout: 5
-    fallback: implement
-
-  gate_approval:
-    id: gate_approval
-    type: approval
-    description: Optional human checkpoint for high-complexity plans
-    question: |
-      ## Plan
-      {{plan_summary}}
-
-      ## Files to modify
-      {{files_to_modify}}
-
-      ## Files to create
-      {{files_to_create}}
-
-      ## Risks
-      {{risks}}
-
-      Complexity: {{complexity_score}}/10
-
-      Approve this plan?
-    options:
-      - "yes"
-      - "no"
-    routes:
-      "yes": implement
-      "no": end_rejected
-    on_other: end_rejected
-
-  implement:
-    id: implement
-    type: llm
-    description: Write code via fs tools. Bounded tool-call loop.
-    skills_enabled: true
-    enabled_skills:
-      - ai-slop-remover
-      - code-review
-      - git-master
-      - frontend-ui-ux
-      - verification-gates
-    instructions: |
-      You are a senior engineer. Implement the plan by writing code via
-      tools. Follow existing patterns in the codebase.
-
-      ## Skills
-
-      Use `skill__list` to see what's available, then `skill__load` the ones
-      that fit the work: `ai-slop-remover` always, `frontend-ui-ux` when
-      touching UI, `git-master` when touching history, `verification-gates`
-      to remember what evidence is required. Unload when a phase ends.
-
-      ## Writing code
-
-      1. Use `fs_patch` for surgical edits to existing files.
-      2. Use `fs_write` for new files or full rewrites.
-      3. NEVER write files via `execute_command`. Do not use `cat >`,
-         `cat >>`, `echo >`, `printf >`, `tee`, heredocs (`<<EOF`), or
-         `python3 -c "open(...).write(...)"`. Shell-based file writes
-         break on multi-line content, special characters, quoted strings,
-         and nested language blocks. `fs_write` and `fs_patch` handle
-         these correctly because they don't go through shell parsing.
-      4. NEVER output code to chat. Always use tools.
-      5. ALWAYS pass ABSOLUTE paths to fs_write and fs_patch. Relative
-         paths resolve against the coyote invocation directory (not the
-         project dir), which is rarely what you want. The project root
-         is {{project_dir}}.
-
-      ## File reading
-
-      1. Use `execute_command` to grep/find:
-         `execute_command --command "grep -rn 'fn handle_request' --include='*.rs' ."`
-         `execute_command --command "find . -name '*.rs' -not -path '*/target/*'"`
-      2. Read only what you need:
-         `fs_cat --path "src/main.rs" --offset 50 --limit 30`
-      3. Never read entire large files. Use offset/limit.
-      4. Use `fs_ls` to list directory contents.
-
-      ## Pattern matching
-
-      Before writing ANY file:
-      1. Find a similar existing file (grep, then read).
-      2. Match its style: imports, naming, structure, error handling.
-      3. Follow the same patterns exactly. Do not invent new ones.
-
-      ## Fix loop
-
-      If the "Fix loop status" section in your user prompt is non-empty,
-      the previous attempt failed verification. Read the error, identify
-      the minimal fix, apply it. Do not refactor while fixing.
-
-      ## Rules
-
-      1. Match existing patterns - read examples first.
-      2. Minimal changes - implement only what's asked.
-      3. Never suppress errors (`as any`, `@ts-ignore`, `#[allow(...)]`
-         on unfamiliar lints, etc.).
-      4. No dead code, no commented-out blocks, no premature abstractions.
-      5. End your turn when editing is done. The graph runs verification next.
-
-      Project directory: {{project_dir}}
-    prompt: |
-      ## Plan summary
-      {{plan_summary}}
-
-      ## Files involved
-      - Modify: {{files_to_modify}}
-      - Create: {{files_to_create}}
-
-      ## Original request from the orchestrator
-      {{initial_prompt}}
-
-      ## Fix loop status
-      {{fix_instructions}}
-    tools:
-      - fs_cat
-      - fs_ls
-      - fs_write
-      - fs_patch
-      - execute_command
-    max_iterations: 30
-    state_updates:
-      last_node_output: "{{output}}"
-    fallback: end_failure
-    next: verify_build
-
-  verify_build:
-    id: verify_build
-    type: script
-    description: Run the project's check/build command. Routes to verify_tests on success, fix_loop_gate on failure.
-    script: scripts/verify_build.sh
-    timeout: 300
-    fallback: fix_loop_gate
-
-  verify_tests:
-    id: verify_tests
-    type: script
-    description: Run the project's test command. Routes to end_success on pass, fix_loop_gate on failure.
-    script: scripts/verify_tests.sh
-    timeout: 600
-    fallback: fix_loop_gate
-
-  fix_loop_gate:
-    id: fix_loop_gate
-    type: script
-    description: Budget gate. Loops back to implement with fix_instructions populated, or terminates as end_failure.
-    script: scripts/fix_loop_gate.sh
-    timeout: 5
-    fallback: end_failure
-
-  self_review:
-    id: self_review
-    type: llm
-    description: Skill-driven self-review of the diff. Catches AI slop, dishonest naming, suppressed errors. Bounded to max_review_attempts.
-    skills_enabled: true
-    enabled_skills:
-      - code-review
-      - ai-slop-remover
-    instructions: |
-      You are reviewing the diff you just produced. Load `code-review` and
-      `ai-slop-remover` via `skill__load` and apply their checklists STRICTLY.
-
-      Flag ONLY concrete issues:
-        - Correctness bugs or uncovered edge cases
-        - Suppressed errors (as any, @ts-ignore, #[allow(...)] on unfamiliar
-          lints, empty catch blocks)
-        - Dishonest naming (get_X that mutates, returns wrong type, etc.)
-        - Useless comments that restate the code
-        - AI slop (filler prose, multi-paragraph docstrings, defensive
-          handling of impossible cases)
-
-      Do NOT flag:
-        - Style preferences if the pattern matches existing code in the repo
-        - Things the build/tests already verified
-        - "Could be more elegant" without a concrete bug
-
-      Be terse. The orchestrator wants signal, not noise. If you find nothing
-      blocking, set review_clean=true and leave review_notes empty.
-
-      Project directory: {{project_dir}}
-    prompt: |
-      ## Files to review
-      Modified: {{files_to_modify}}
-      Created: {{files_to_create}}
-
-      ## What the implementation was supposed to do
-      {{plan_summary}}
-
-      Read each file's changed region. Apply the review skills. Output your verdict.
-    tools:
-      - fs_cat
-      - fs_ls
-      - execute_command
-    max_iterations: 15
-    output_schema:
-      type: object
-      properties:
-        review_clean:
-          type: boolean
-          description: True if no blocker issues were found.
-        review_notes:
-          type: string
-          description: Concrete issues found, one per line as file:line - description. Empty when review_clean is true.
-      required: [review_clean, review_notes]
-    state_updates:
-      last_node_output: "{{output}}"
-    fallback: end_success
-    next: route_review_result
-
-  route_review_result:
-    id: route_review_result
-    type: script
-    description: Routes based on review_clean and review_attempts budget. End on clean or budget exhausted; loop to implement otherwise.
-    script: scripts/route_review_result.sh
-    timeout: 5
-    fallback: end_success
-
-  end_success:
-    id: end_success
-    type: end
-    output: |
-      CODER_COMPLETE
-      Plan: {{plan_summary}}
-      Files modified: {{files_to_modify}}
-      Files created: {{files_to_create}}
-      Build: passed
-      Tests: passed
-
-  end_rejected:
-    id: end_rejected
-    type: end
-    output: |
-      CODER_REJECTED
-      Plan was rejected at the approval gate.
-      Plan: {{plan_summary}}
-
-  end_failure:
-    id: end_failure
-    type: end
-    output: |
-      CODER_FAILED
-      Plan: {{plan_summary}}
-      Attempts: {{fix_attempts}}/{{max_fix_attempts}}
-
-      Last node output:
-      {{last_node_output}}
-
-      Last build output:
-      {{build_output}}
-
-      Last tests output:
-      {{tests_output}}

assets/agents/coder/scripts/fix_loop_gate.sh

@@ -1,49 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-if [[ -n "${GRAPH_STATE_FILE:-}" ]]; then
-  state=$(cat "$GRAPH_STATE_FILE")
-elif [[ -n "${GRAPH_STATE:-}" ]]; then
-  state="$GRAPH_STATE"
-else
-  state='{}'
-fi
-
-fix_attempts=$(echo "$state" | jq -r '.fix_attempts // 0')
-max_fix_attempts=$(echo "$state" | jq -r '.max_fix_attempts // 3')
-build_ok=$(echo "$state" | jq -r '.build_ok | if . == null then "true" else (. | tostring) end')
-tests_ok=$(echo "$state" | jq -r '.tests_ok | if . == null then "true" else (. | tostring) end')
-build_output=$(echo "$state" | jq -r '.build_output // ""')
-tests_output=$(echo "$state" | jq -r '.tests_output // ""')
-
-if (( fix_attempts >= max_fix_attempts )); then
-  jq -nc \
-    --argjson n "$fix_attempts" \
-    '{
-      "fix_attempts": $n,
-      "_next": "end_failure"
-    }'
-  exit 0
-fi
-
-next_attempts=$((fix_attempts + 1))
-
-if [[ "$build_ok" != "true" ]]; then
-  fix_instructions=$(printf '## Fix loop status (attempt %d of %d)\n\nThe previous attempt failed the build.\n\nBuild output:\n```\n%s\n```\n\nIdentify the minimal fix and apply it. Do not refactor.' \
-    "$next_attempts" "$max_fix_attempts" "$build_output")
-elif [[ "$tests_ok" != "true" ]]; then
-  fix_instructions=$(printf '## Fix loop status (attempt %d of %d)\n\nBuild passed but tests failed.\n\nTest output:\n```\n%s\n```\n\nIdentify the minimal fix and apply it. Do not refactor.' \
-    "$next_attempts" "$max_fix_attempts" "$tests_output")
-else
-  fix_instructions=$(printf '## Fix loop status (attempt %d of %d)\n\nfix_loop_gate was reached but no failure was detected in state. Re-run the verification step.' \
-    "$next_attempts" "$max_fix_attempts")
-fi
-
-jq -nc \
-  --argjson n "$next_attempts" \
-  --arg fi "$fix_instructions" \
-  '{
-    "fix_attempts": $n,
-    "fix_instructions": $fi,
-    "_next": "implement"
-  }'

assets/agents/coder/scripts/resolve_paths.sh

@@ -1,12 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-project_dir="${LLM_AGENT_VAR_PROJECT_DIR:-.}"
-resolved=$(cd "$project_dir" 2>/dev/null && pwd) || resolved="$project_dir"
-
-jq -nc \
-  --arg pd "$resolved" \
-  '{
-    "project_dir": $pd,
-    "_next": "analyze_request"
-  }'

assets/agents/coder/scripts/route_complexity.sh

@@ -1,23 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-if [[ -n "${GRAPH_STATE_FILE:-}" ]]; then
-  state=$(cat "$GRAPH_STATE_FILE")
-elif [[ -n "${GRAPH_STATE:-}" ]]; then
-  state="$GRAPH_STATE"
-else
-  state='{}'
-fi
-
-complexity=$(echo "$state" | jq -r '.complexity_score // 0')
-
-if [[ "${CODER_AUTOAPPROVE:-0}" == "1" ]]; then
-  jq -nc '{"_next": "implement"}'
-  exit 0
-fi
-
-if (( complexity >= 7 )); then
-  jq -nc '{"_next": "gate_approval"}'
-else
-  jq -nc '{"_next": "implement"}'
-fi

assets/agents/coder/scripts/route_review_result.sh

@@ -1,58 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-if [[ -n "${GRAPH_STATE_FILE:-}" ]]; then
-  state=$(cat "$GRAPH_STATE_FILE")
-elif [[ -n "${GRAPH_STATE:-}" ]]; then
-  state="$GRAPH_STATE"
-else
-  state='{}'
-fi
-
-review_clean=$(echo "$state" | jq -r '.review_clean // true')
-review_attempts=$(echo "$state" | jq -r '.review_attempts // 0')
-max_review_attempts=$(echo "$state" | jq -r '.max_review_attempts // 1')
-review_notes=$(echo "$state" | jq -r '.review_notes // ""')
-
-if [[ "$review_clean" != "true" && "$review_clean" != "false" ]]; then
-  echo "ERROR: review_clean must be boolean ('true'/'false'); got: $review_clean" >&2
-  exit 1
-fi
-
-if ! [[ "$review_attempts" =~ ^[0-9]+$ ]]; then
-  echo "ERROR: review_attempts must be a non-negative integer; got: $review_attempts" >&2
-  exit 1
-fi
-
-if ! [[ "$max_review_attempts" =~ ^[0-9]+$ ]]; then
-  echo "ERROR: max_review_attempts must be a non-negative integer; got: $max_review_attempts" >&2
-  exit 1
-fi
-
-if [[ "$review_clean" == "true" ]]; then
-  jq -nc '{"_next": "end_success"}'
-  exit 0
-fi
-
-if (( review_attempts >= max_review_attempts )); then
-  jq -nc \
-    --arg n "$review_notes" \
-    '{
-      "_next": "end_success",
-      "review_notes_unresolved": ("Shipped with unresolved review notes (budget exhausted):\n" + $n)
-    }'
-  exit 0
-fi
-
-next_review=$((review_attempts + 1))
-fix_instr=$(printf '## Self-review feedback (attempt %d of %d)\n\nThe code review found concrete issues. Address them with minimal edits. Do not refactor unrelated code.\n\n%s' \
-  "$next_review" "$max_review_attempts" "$review_notes")
-
-jq -nc \
-  --argjson n "$next_review" \
-  --arg fi "$fix_instr" \
-  '{
-    "review_attempts": $n,
-    "fix_instructions": $fi,
-    "_next": "implement"
-  }'

assets/agents/coder/scripts/verify_build.sh

@@ -1,55 +0,0 @@
-#!/usr/bin/env bash
-set -uo pipefail
-
-# shellcheck disable=SC1091
-source "$(dirname "$0")/../../.shared/utils.sh"
-
-if [[ -n "${GRAPH_STATE_FILE:-}" ]]; then
-  state=$(cat "$GRAPH_STATE_FILE")
-elif [[ -n "${GRAPH_STATE:-}" ]]; then
-  state="$GRAPH_STATE"
-else
-  state='{}'
-fi
-
-project_dir=$(echo "$state" | jq -r '.project_dir // "."')
-
-if [[ -n "${BUILD_CMD:-}" ]]; then
-  cmd="$BUILD_CMD"
-else
-  project_info=$(detect_project "$project_dir")
-  cmd=$(echo "$project_info" | jq -r '.check // .build // ""')
-fi
-
-if [[ -z "$cmd" || "$cmd" == "null" ]]; then
-  jq -nc '{
-    "build_ok": true,
-    "build_output": "(no build/check command available for this project type)",
-    "_next": "verify_tests"
-  }'
-  exit 0
-fi
-
-exit_code=0
-output=$(cd "$project_dir" && eval "$cmd" 2>&1) || exit_code=$?
-
-if (( exit_code == 0 )); then
-  jq -nc \
-    --arg out "$output" \
-    --arg cmd "$cmd" \
-    '{
-      "build_ok": true,
-      "build_output": ("Ran: " + $cmd + "\n\n" + $out),
-      "_next": "verify_tests"
-    }'
-else
-  jq -nc \
-    --arg out "$output" \
-    --arg cmd "$cmd" \
-    --argjson rc "$exit_code" \
-    '{
-      "build_ok": false,
-      "build_output": ("Ran: " + $cmd + "\nExit code: " + ($rc | tostring) + "\n\n" + $out),
-      "_next": "fix_loop_gate"
-    }'
-fi

assets/agents/coder/scripts/verify_tests.sh

@@ -1,55 +0,0 @@
-#!/usr/bin/env bash
-set -uo pipefail
-
-# shellcheck disable=SC1091
-source "$(dirname "$0")/../../.shared/utils.sh"
-
-if [[ -n "${GRAPH_STATE_FILE:-}" ]]; then
-  state=$(cat "$GRAPH_STATE_FILE")
-elif [[ -n "${GRAPH_STATE:-}" ]]; then
-  state="$GRAPH_STATE"
-else
-  state='{}'
-fi
-
-project_dir=$(echo "$state" | jq -r '.project_dir // "."')
-
-if [[ -n "${TEST_CMD:-}" ]]; then
-  cmd="$TEST_CMD"
-else
-  project_info=$(detect_project "$project_dir")
-  cmd=$(echo "$project_info" | jq -r '.test // ""')
-fi
-
-if [[ -z "$cmd" || "$cmd" == "null" ]]; then
-  jq -nc '{
-    "tests_ok": true,
-    "tests_output": "(no test command available for this project type)",
-    "_next": "self_review"
-  }'
-  exit 0
-fi
-
-exit_code=0
-output=$(cd "$project_dir" && eval "$cmd" 2>&1) || exit_code=$?
-
-if (( exit_code == 0 )); then
-  jq -nc \
-    --arg out "$output" \
-    --arg cmd "$cmd" \
-    '{
-      "tests_ok": true,
-      "tests_output": ("Ran: " + $cmd + "\n\n" + $out),
-      "_next": "self_review"
-    }'
-else
-  jq -nc \
-    --arg out "$output" \
-    --arg cmd "$cmd" \
-    --argjson rc "$exit_code" \
-    '{
-      "tests_ok": false,
-      "tests_output": ("Ran: " + $cmd + "\nExit code: " + ($rc | tostring) + "\n\n" + $out),
-      "_next": "fix_loop_gate"
-    }'
-fi

assets/agents/coder/tools.sh

@@ -14,6 +14,99 @@ _project_dir() {
   (cd "${dir}" 2>/dev/null && pwd) || echo "${dir}"
 }
 
+# Normalize a path to be relative to project root.
+# Strips the project_dir prefix if the LLM passes an absolute path.
+# Usage: local rel_path; rel_path=$(_normalize_path "/abs/or/rel/path")
+_normalize_path() {
+  local input_path="$1"
+  local project_dir
+  project_dir=$(_project_dir)
+
+  if [[ "${input_path}" == /* ]]; then
+    input_path="${input_path#"${project_dir}"/}"
+  fi
+
+  input_path="${input_path#./}"
+  echo "${input_path}"
+}
+
+# @cmd Read a file's contents before modifying
+# @option --path! Path to the file (relative to project root)
+read_file() {
+  local file_path
+	# shellcheck disable=SC2154
+  file_path=$(_normalize_path "${argc_path}")
+  local project_dir
+  project_dir=$(_project_dir)
+  local full_path="${project_dir}/${file_path}"
+  
+  if [[ ! -f "${full_path}" ]]; then
+    warn "File not found: ${file_path}" >> "$LLM_OUTPUT"
+    return 0
+  fi
+  
+  {
+  	info "Reading: ${file_path}"
+  	echo ""
+  	cat "${full_path}"
+  } >> "$LLM_OUTPUT"
+}
+
+# @cmd Write complete file contents
+# @option --path! Path for the file (relative to project root)
+# @option --content! Complete file contents to write
+write_file() {
+  local file_path
+  file_path=$(_normalize_path "${argc_path}")
+  # shellcheck disable=SC2154
+  local content="${argc_content}"
+  local project_dir
+  project_dir=$(_project_dir)
+  local full_path="${project_dir}/${file_path}"
+  
+  mkdir -p "$(dirname "${full_path}")"
+  printf '%s' "${content}" > "${full_path}"
+  
+  green "Wrote: ${file_path}" >> "$LLM_OUTPUT"
+}
+
+# @cmd Find files similar to a given path (for pattern matching)
+# @option --path! Path to find similar files for
+find_similar_files() {
+  local file_path
+  file_path=$(_normalize_path "${argc_path}")
+  local project_dir
+  project_dir=$(_project_dir)
+  
+  local ext="${file_path##*.}"
+  local dir
+  dir=$(dirname "${file_path}")
+  
+  info "Similar files to: ${file_path}" >> "$LLM_OUTPUT"
+  echo "" >> "$LLM_OUTPUT"
+  
+  local results
+  results=$(find "${project_dir}/${dir}" -maxdepth 1 -type f -name "*.${ext}" \
+    ! -name "$(basename "${file_path}")" \
+    ! -name "*test*" \
+    ! -name "*spec*" \
+    2>/dev/null | sed "s|^${project_dir}/||" | head -3)
+  
+  if [[ -z "${results}" ]]; then
+    results=$(find "${project_dir}/src" -type f -name "*.${ext}" \
+      ! -name "*test*" \
+      ! -name "*spec*" \
+      -not -path '*/target/*' \
+      2>/dev/null | sed "s|^${project_dir}/||" | head -3)
+  fi
+  
+  if [[ -n "${results}" ]]; then
+    echo "${results}" >> "$LLM_OUTPUT"
+  else
+    warn "No similar files found" >> "$LLM_OUTPUT"
+  fi
+}
+
 # @cmd Verify the project builds successfully
 verify_build() {
   local project_dir
@@ -96,3 +189,28 @@ get_project_structure() {
   } >> "$LLM_OUTPUT"
 }
 
+# @cmd Search for content in the codebase
+# @option --pattern! Pattern to search for
+search_code() {
+	# shellcheck disable=SC2154
+  local pattern="${argc_pattern}"
+  local project_dir
+  project_dir=$(_project_dir)
+  
+  info "Searching: ${pattern}" >> "$LLM_OUTPUT"
+  echo "" >> "$LLM_OUTPUT"
+  
+  local results
+  results=$(grep -rn "${pattern}" "${project_dir}" 2>/dev/null | \
+    grep -v '/target/' | \
+    grep -v '/node_modules/' | \
+    grep -v '/.git/' | \
+    sed "s|^${project_dir}/||" | \
+    head -20) || true
+  
+  if [[ -n "${results}" ]]; then
+    echo "${results}" >> "$LLM_OUTPUT"
+  else
+    warn "No matches" >> "$LLM_OUTPUT"
+  fi
+}

assets/agents/deep-research/README.md

@@ -1,274 +0,0 @@
-# deep-research
-
-A deep web research agent, built as a Coyote graph agent. It plans an
-investigation, decomposes it into sub-questions researched in
-parallel, grounds the work in a local knowledge corpus, vets the
-credibility of cited sources, runs a reflexion self-critique loop to
-revise weak findings, delegates the final write-up to a focused
-sub-agent, checks that the cited sources are reachable, and gates the
-result behind human approval.
-
-Unlike a regular agent (which takes a goal and improvises the steps),
-this agent runs a fixed graph: every request goes through the same
-`plan -> parallel research -> vet -> critique -> synthesize -> verify -> approve`
-pipeline.
-
-This agent is also the **canonical reference for the Coyote graph
-system**: it exercises every node type (`script`, `llm`, `rag`, `map`,
-`agent`, `input`, `approval`, `end`) and both static fan-out and
-dynamic `map` fan-out. If you are learning how to build a graph
-agent, this is the file to read alongside the
-[Graph-Agents wiki](https://github.com/Dark-Alex-17/coyote/wiki/Graph-Agents).
-
-## Workflow
-
-17 nodes. `->` is the static route; a script node can also route
-dynamically via `_next`. The `▶▶` line is a parallel super-step —
-those branches run concurrently:
-
-```
-parse_request (script)              -> bootstrap_research   (or -> ask_topic if no topic)
-ask_topic (input)                   -> bootstrap_research
-bootstrap_research (script)         -> [plan, knowledge_lookup]   ▶▶ parallel
-plan (llm + output_schema)          -> research_each_question
-knowledge_lookup (rag)              -> research_each_question
-research_each_question (map)        -> combine_findings    (spawns one branch per question)
-  └─ research_one_question (llm)    (atomic; runs N×, joins at map)
-combine_findings (script)           -> vet_sources
-vet_sources (llm + custom tool)     -> critique
-critique (llm)                      -> reflexion_gate
-reflexion_gate (script)             -> synthesize  (or -> research_each_question: reflexion loop)
-synthesize (agent: report-writer)   -> verify_sources
-verify_sources (script)             -> approve
-approve (approval)                  -> end_accepted          ("accept")
-                                    -> end_rejected          ("reject")
-                                    -> incorporate_feedback   (any free-form answer)
-incorporate_feedback (script)       -> research_each_question (the human-feedback loop)
-```
-
-### Node-type breakdown
-
-| Type                        | Nodes                                                                                                                 |
-|-----------------------------|-----------------------------------------------------------------------------------------------------------------------|
-| `script` (Python)           | `parse_request`, `bootstrap_research`, `combine_findings`, `reflexion_gate`, `verify_sources`, `incorporate_feedback` |
-| `llm` (tools: `[]`)         | `plan`, `critique`                                                                                                    |
-| `llm` (with tool whitelist) | `research_one_question`, `vet_sources`                                                                                |
-| `rag`                       | `knowledge_lookup` — local corpus retrieval                                                                           |
-| `map`                       | `research_each_question` — dynamic fan-out per sub-question                                                           |
-| `agent`                     | `synthesize` — spawns the `report-writer` sub-agent                                                                   |
-| `input`                     | `ask_topic`                                                                                                           |
-| `approval`                  | `approve`                                                                                                             |
-| `end`                       | `end_accepted`, `end_rejected`                                                                                        |
-
-## Parallel execution
-
-The graph has two parallel super-steps where Coyote's BSP scheduler runs
-branches concurrently.
-
-**1. Context loading (`plan` ‖ `knowledge_lookup`)** — after
-`bootstrap_research`, the LLM planner (which decomposes the topic into
-sub-questions) and the RAG retrieval over the local `knowledge/`
-corpus run side by side. They write disjoint state keys (`plan` writes
-`research_plan` and `questions`; `knowledge_lookup` writes
-`local_context` and `local_sources`) so no reducer is needed.
-
-**2. Per-question research (`research_each_question` map)** — the
-plan emits a `questions` array (3-5 entries, enforced by its
-`output_schema`). The `map` node spawns one parallel branch per
-question (`max_concurrency: 3`). Each branch is an isolated
-`research_one_question` LLM invocation with web tools, instructed to
-investigate exactly its assigned question. Outputs collect into
-`question_findings` in input order, then `combine_findings` joins
-them into a single `findings` Markdown document for downstream nodes.
-
-`settings.max_concurrency: 4` is the graph-wide cap; the per-`map`
-override (`max_concurrency: 3` on `research_each_question`) is
-deliberately lower to leave headroom for the planner's tool calls
-running alongside RAG.
-
-## Local knowledge corpus
-
-`knowledge_lookup` is a `rag` node — it runs hybrid (vector + keyword)
-retrieval over every file in `knowledge/`. The directory ships with a
-small `research-style-notes.md` so the RAG node has something to
-retrieve against on a clean install; drop your own Markdown notes,
-PDFs, or text files into `knowledge/` to bias the research toward
-your local context.
-
-The knowledge base is built once, at agent-load time, into
-`~/.config/coyote/agents/deep-research/knowledge_lookup.yaml`. Because
-the node fully specifies its build config (`embedding_model`,
-`chunk_size`, `chunk_overlap`), the build is non-interactive. Delete
-that cached file after adding or changing knowledge to force a
-rebuild.
-
-## Sub-agent: report-writer
-
-The `synthesize` node is an `agent` node that spawns the
-`report-writer` sub-agent (`assets/agents/report-writer/`). This is
-the agent-as-tool pattern: the orchestrating graph delegates the
-writing phase to a focused sub-agent dedicated to coherent prose,
-while the research phase uses different (typically cheaper) LLM nodes
-for fast-and-many-question investigation.
-
-The `report-writer` sub-agent has no tools — it cannot access the
-web, cannot search, and cannot invent facts. It reads only the
-findings it is given and produces a final Markdown report preserving
-every inline citation. See `assets/agents/report-writer/README.md`
-for details.
-
-## Tools and tool scoping
-
-This agent demonstrates Coyote's three tool sources and how an `llm`
-node's `tools:` whitelist scopes them per node.
-
-The agent's full tool universe, declared in `graph.yaml`:
-
-- **Global tools** (`global_tools`): `web_search_coyote`,
-  `fetch_url_via_curl`, `search_arxiv` - Coyote's built-in tool scripts.
-- **MCP server** (`mcp_servers`): `ddg-search` - a DuckDuckGo web
-  search MCP server. Referenced in a whitelist as `mcp:ddg-search`.
-- **Custom agent tool** (`tools.sh`): `classify_source` - a
-  deterministic source-credibility classifier shipped with this agent.
-
-No node receives all of these. Each `llm` node's `tools:` whitelist
-narrows the universe to exactly what that step needs:
-
-| Node                    | `tools:` whitelist                                                          | Draws from               |
-|-------------------------|-----------------------------------------------------------------------------|--------------------------|
-| `plan`, `critique`      | `[]`                                                                        | nothing - pure reasoning |
-| `research_one_question` | `web_search_coyote`, `fetch_url_via_curl`, `search_arxiv`, `mcp:ddg-search` | global tools + MCP       |
-| `vet_sources`           | `classify_source`                                                           | the custom tool only     |
-
-`research_one_question` (each parallel branch of the map) can search
-and fetch but cannot classify sources; `vet_sources` can classify
-sources but cannot touch the web. That separation is the point of the
-`tools:` whitelist: a node gets only the tools its job calls for,
-never the agent's full set.
-
-The `classify_source` custom tool (`tools.sh`) takes a URL and returns
-a credibility tier (government, academic, preprint, organization,
-unverified) derived from the host and top-level domain. It is
-deterministic - exactly the kind of logic a tool should own rather than
-the LLM guessing.
-
-Web search may require API-key configuration; see the
-[Tools](https://github.com/Dark-Alex-17/coyote/wiki/Tools) docs.
-`fetch_url_via_curl`, `search_arxiv`, and `classify_source` work
-without a key.
-
-## Setup
-
-`research_one_question` (each parallel branch of the `map`) uses the
-`ddg-search` MCP server via `mcp:ddg-search`. It is one of Coyote's
-default MCP servers; make sure it is registered in
-`~/.config/coyote/mcp.json` (run `coyote --install mcp_config` to restore
-the default template if it is missing). If `ddg-search` is unavailable,
-the branches still have their global web-search tools to fall back on.
-
-The `synthesize` node spawns the `report-writer` sub-agent. Both
-agents ship with `coyote agents install`; if you install one manually,
-install both so the agent reference resolves.
-
-## Reflexion
-
-The agent has two loops, both built with script nodes that route via
-`_next`. The engine allows back-edges at runtime; the validator only
-rejects cycles built from static `next` / `routes` edges, so script
-`_next` loops are always allowed.
-
-**Automated reflexion loop.** After the parallel research map and
-`vet_sources`, the `critique` node reviews the merged findings
-against the research plan and the source credibility assessment, and
-emits `VERDICT: PASS` or `VERDICT: REVISE` with specific feedback.
-`reflexion_gate.py` then:
-
-- `PASS` -> continue to `synthesize`.
-- `REVISE`, budget remaining -> loop back to `research_each_question`,
-  with the critique injected as `research_feedback` so every parallel
-  branch sees it on the retry.
-- `REVISE`, budget spent -> continue to `synthesize` anyway (the human
-  approval step is the final backstop).
-
-The budget is `MAX_REFLEXION_REVISIONS` in `reflexion_gate.py`
-(default 2, so the research map runs at most 3 times per pass).
-
-**Human-feedback loop.** At `approve` the user answers `accept`,
-`reject`, or types their own feedback. A free-form answer routes via
-the approval node's `on_other` to `incorporate_feedback.py`, which
-folds that text into `research_feedback` and loops back to
-`research_each_question` for another parallel pass.
-
-`settings.max_loop_iterations` (40) is the engine's infinite-loop
-backstop: it caps the total visits to any single node.
-
-## Running
-
-```sh
-coyote agents install                  # ships deep-research
-coyote -a deep-research "How does HTTP/3 differ from HTTP/2?"
-coyote -a deep-research "Recent advances in solid-state batteries"
-coyote -a deep-research                # no prompt -> triggers ask_topic
-```
-
-## Anti-hallucination
-
-- `research_one_question` (each map branch) is instructed to back
-  every claim with a real retrieved source and never to fabricate
-  URLs, titles, or DOIs.
-- `vet_sources` classifies every cited source so weak sources are
-  visible to the critique step.
-- `critique` independently reviews the merged findings and sends weak
-  or uncited work back for another parallel research pass.
-- `synthesize` (the `report-writer` sub-agent) is grounded: it may use
-  only the gathered findings and must keep each claim's inline source.
-  It has no tools and cannot browse the web.
-- `verify_sources` probes every cited URL / DOI with an HTTP HEAD
-  request and reports which are unreachable, so the human reviewer
-  sees broken citations before approving.
-
-## Customizing
-
-- **Loop budget.** `MAX_REFLEXION_REVISIONS` in `reflexion_gate.py`.
-- **Map concurrency.** The `research_each_question` node's
-  `max_concurrency: 3` caps simultaneous web-research branches.
-  Raise to investigate more questions in parallel; lower to be gentle
-  on rate-limited providers.
-- **Per-node model.** Add `model: anthropic:...` to any `llm` node.
-  Cheap models work well for `plan` / `critique` / `vet_sources`; the
-  heavy intelligence is needed in `research_one_question` and the
-  `report-writer` sub-agent.
-- **Tool scope.** Narrow the `research_one_question` node's `tools:`
-  list to constrain where each branch looks (for example, drop
-  `web_search_coyote` and `mcp:ddg-search` to force arXiv-only
-  research).
-- **Local knowledge.** Drop files into `knowledge/` to bias every
-  research branch toward your local context (see the *Local
-  knowledge corpus* section above).
-- **Different writer.** Replace `agent: report-writer` on the
-  `synthesize` node with the name of any other agent. The
-  orchestrator does not care what kind of agent the writer is.
-- **Skip approval.** Point both `approve` routes at `end_accepted`,
-  or wire `verify_sources` straight to an `end` node.
-
-## Files
-
-```
-assets/agents/deep-research/
-  graph.yaml                    - agent config + 17-node workflow
-  tools.sh                      - classify_source custom tool
-  README.md                     - this file
-  knowledge/
-    README.md                   - corpus-format notes
-    research-style-notes.md     - starter knowledge file (replace with your notes)
-  scripts/
-    parse_request.py            - _next: bootstrap_research, or ask_topic if no topic
-    bootstrap_research.py       - fan-out source: next [plan, knowledge_lookup]
-    combine_findings.py         - joins map output (question_findings) into findings
-    reflexion_gate.py           - _next: research_each_question (revise) or synthesize
-    verify_sources.py           - HTTP HEAD on cited URLs / DOIs
-    incorporate_feedback.py     - _next: research_each_question, with user feedback
-```
-
-See also `assets/agents/report-writer/` — the sub-agent the
-`synthesize` node spawns.

assets/agents/deep-research/graph.yaml

@@ -1,291 +0,0 @@
-name: deep-research
-description: |
-  Deep web research workflow. Plans an investigation, decomposes it
-  into sub-questions researched in parallel, grounds the work in a
-  local knowledge corpus, vets the credibility of cited sources, runs
-  a reflexion self-critique loop to revise weak or incomplete findings,
-  delegates the final write-up to a focused sub-agent, checks that the
-  cited sources are reachable, and gates the result behind human
-  approval. A reviewer's free-form feedback at the approval step feeds
-  back into another research pass.
-
-  This is the canonical Coyote graph-agent reference: it exercises every
-  node type (script, llm, rag, map, agent, input, approval, end) and
-  both static fan-out and dynamic map fan-out.
-
-version: "1.0"
-
-global_tools:
-  - web_search_coyote.sh
-  - fetch_url_via_curl.sh
-  - search_arxiv.sh
-
-mcp_servers:
-  - ddg-search
-
-conversation_starters:
-  - "How does HTTP/3 differ from HTTP/2?"
-  - "Summarize recent advances in solid-state battery chemistry"
-
-settings:
-  max_loop_iterations: 40
-  log_state_snapshots: false
-  validate_before_run: true
-  max_concurrency: 4
-
-initial_state:
-  research_feedback: ""
-  research_attempts: 0
-  local_context: ""
-  local_sources: ""
-
-start: parse_request
-
-nodes:
-
-  parse_request:
-    id: parse_request
-    type: script
-    script: scripts/parse_request.py
-    next: bootstrap_research
-
-  ask_topic:
-    id: ask_topic
-    type: input
-    question: "What would you like me to research?"
-    validation: "len(input) > 0"
-    state_updates:
-      topic: "{{input}}"
-    next: bootstrap_research
-
-  bootstrap_research:
-    id: bootstrap_research
-    type: script
-    script: scripts/bootstrap_research.py
-    next: [plan, knowledge_lookup]
-
-  plan:
-    id: plan
-    type: llm
-    instructions: |
-      You are a research planner. Given a topic, produce a focused
-      research plan and decompose it into 3-5 specific sub-questions
-      that can each be researched independently in parallel.
-
-      The plan is a short narrative naming the key questions and the
-      kinds of sources that would be authoritative. The sub-questions
-      are precise, self-contained queries (each one is sent on its own
-      to a separate research worker, so they must be answerable
-      without each other's context).
-    prompt: "Research topic: {{topic}}"
-    tools: []
-    output_schema:
-      type: object
-      properties:
-        research_plan:
-          type: string
-          description: A short plan narrative.
-        questions:
-          type: array
-          items: { type: string }
-          minItems: 1
-          maxItems: 6
-          description: 3-5 specific, self-contained sub-questions.
-      required: [research_plan, questions]
-    next: research_each_question
-
-  knowledge_lookup:
-    id: knowledge_lookup
-    type: rag
-    documents:
-      - ./knowledge/
-    query: "{{topic}}"
-    top_k: 6
-    chunk_size: 1000
-    chunk_overlap: 100
-    state_updates:
-      local_context: "{{output.context}}"
-      local_sources: "{{output.sources}}"
-    next: research_each_question
-
-  research_each_question:
-    id: research_each_question
-    type: map
-    over: "{{questions}}"
-    as: question
-    branch: research_one_question
-    collect_into: question_findings
-    max_concurrency: 3
-    next: combine_findings
-
-  research_one_question:
-    id: research_one_question
-    type: llm
-    instructions: |
-      You are a web research assistant. Investigate the SINGLE question
-      given to you using your tools: search the web, fetch and read
-      pages, and search arXiv for academic sources.
-
-      Rules:
-        - Every factual claim must be backed by a real source you
-          actually retrieved. Never fabricate URLs, page titles,
-          authors, or DOIs.
-        - Prefer primary and authoritative sources over aggregators.
-        - Where sources disagree, report the disagreement rather than
-          papering over it.
-        - Put the URL (or DOI) inline next to each claim it supports.
-
-      Return organized findings in plain text. Do not include
-      meta-commentary about the process.
-    prompt: |
-      Research question: {{question}}
-
-      Local context that may help:
-      {{local_context}}
-
-      {{research_feedback}}
-    tools:
-      - web_search_coyote
-      - fetch_url_via_curl
-      - search_arxiv
-      - mcp:ddg-search
-    max_iterations: 10
-    max_attempts: 2
-    temperature: 0.1
-
-  combine_findings:
-    id: combine_findings
-    type: script
-    script: scripts/combine_findings.py
-    next: vet_sources
-
-  vet_sources:
-    id: vet_sources
-    type: llm
-    instructions: |
-      You assess the credibility of the sources cited in a set of
-      research findings. For every distinct source URL in the findings,
-      call the `classify_source` tool to get its credibility tier. Then
-      summarize: which claims rest on HIGH-credibility sources, and
-      which rest on PREPRINT or UNVERIFIED sources and so need
-      corroboration. Do NOT do any new research -- assess only what is
-      already cited.
-    prompt: |
-      Findings to assess:
-      {{findings}}
-    tools:
-      - classify_source
-    max_iterations: 15
-    state_updates:
-      source_assessment: "{{output}}"
-    next: critique
-
-  critique:
-    id: critique
-    type: llm
-    instructions: |
-      You are a meticulous research reviewer. Judge whether the
-      findings below are good enough to synthesize a complete,
-      well-supported report that answers the research plan.
-
-      Mark the findings REVISE if ANY of these hold:
-        - A research-plan question is unanswered or only weakly
-          addressed.
-        - A factual claim has no source, or cites a source that looks
-          fabricated.
-        - The findings lean on a single source where corroboration is
-          needed.
-        - A key claim rests only on a PREPRINT or UNVERIFIED source,
-          per the source credibility assessment below.
-        - An obvious counter-perspective or recent development is
-          missing.
-      Otherwise mark them PASS.
-
-      Respond in EXACTLY this format, nothing else:
-
-      VERDICT: <PASS or REVISE>
-      FEEDBACK: <if REVISE, be specific and actionable -- name the gaps
-      and what kind of source would close them; if PASS, write "none">
-    prompt: |
-      Research plan:
-      {{research_plan}}
-
-      Findings under review:
-      {{findings}}
-
-      Source credibility assessment:
-      {{source_assessment}}
-    tools: []
-    state_updates:
-      critique: "{{output}}"
-    next: reflexion_gate
-
-  reflexion_gate:
-    id: reflexion_gate
-    type: script
-    script: scripts/reflexion_gate.py
-    next: synthesize
-
-  synthesize:
-    id: synthesize
-    type: agent
-    agent: report-writer
-    prompt: |
-      Research topic: {{topic}}
-
-      Findings (organized by sub-question, with inline citations):
-      {{findings}}
-
-      Source credibility assessment:
-      {{source_assessment}}
-
-      Produce the final report following your instructions.
-    timeout: 300
-    state_updates:
-      report: "{{output}}"
-    next: verify_sources
-
-  verify_sources:
-    id: verify_sources
-    type: script
-    script: scripts/verify_sources.py
-    next: approve
-
-  approve:
-    id: approve
-    type: approval
-    question: |
-      Research report on: {{topic}}
-
-      {{report}}
-
-      ----
-      {{source_check}}
-      ----
-
-      Accept this report? Pick "accept" or "reject", or type specific
-      feedback to send the research back for another pass.
-    options:
-      - "accept"
-      - "reject"
-    routes:
-      "accept": end_accepted
-      "reject": end_rejected
-    on_other: incorporate_feedback
-    state_updates:
-      decision: "{{choice}}"
-
-  incorporate_feedback:
-    id: incorporate_feedback
-    type: script
-    script: scripts/incorporate_feedback.py
-
-  end_accepted:
-    id: end_accepted
-    type: end
-    output: "{{report}}"
-
-  end_rejected:
-    id: end_rejected
-    type: end
-    output: "Research on '{{topic}}' was rejected and discarded."

assets/agents/deep-research/knowledge/README.md

@@ -1,23 +0,0 @@
-# Local knowledge corpus for deep-research
-
-The `knowledge_lookup` node in `graph.yaml` is a `rag` node that runs
-hybrid (vector + keyword) retrieval over every file in this directory.
-Drop your own notes, papers (PDFs), Markdown docs, or text files here
-and they will be indexed into a per-agent knowledge base on first run.
-
-Coyote supports common file types out of the box: `.md`, `.txt`, `.pdf`,
-`.html`, and others. Subdirectories are walked recursively.
-
-A small starter file (`research-style-notes.md`) ships so the RAG
-node has something non-empty to retrieve against on a clean install.
-Replace or extend it with your own materials to bias the research
-phase toward your local context.
-
-To force the knowledge base to rebuild after you add or change files,
-delete the cached index:
-
-```sh
-rm ~/.config/coyote/agents/deep-research/knowledge_lookup.yaml
-```
-
-The next run will rebuild from the current contents of this directory.

assets/agents/deep-research/knowledge/research-style-notes.md

@@ -1,49 +0,0 @@
-# Research style notes
-
-These are general principles the `deep-research` agent should keep in
-mind regardless of topic. Replace this file with your own notes if you
-want to bias retrieval toward your local context.
-
-## What "good research" means here
-
-- **Every factual claim cites a source you actually retrieved.** Never
-  fabricate URLs, page titles, authors, or DOIs.
-- **Primary sources beat aggregators.** Prefer the original paper, the
-  RFC, the standards body, or the manufacturer over a blog summarizing
-  them.
-- **Corroboration matters where stakes are high.** If a single source
-  makes a strong claim, look for a second independent source before
-  taking it as established.
-- **Disagreement is information, not noise.** If two credible sources
-  disagree, report the disagreement and the reasoning on each side.
-- **Old does not mean wrong.** A 2014 RFC is still authoritative if no
-  newer one has obsoleted it; check before assuming a source is stale.
-
-## Source-tier heuristics
-
-The `vet_sources` node uses these rough tiers to weigh credibility.
-The custom tool `classify_source` (see `tools.sh`) implements this
-deterministically by hostname / TLD.
-
-- **HIGH:** government domains (`.gov`, `.mil`), academic institutions
-  (`.edu`, university subdomains), peer-reviewed journals, standards
-  bodies (IETF/RFCs, W3C, ISO, IEEE, NIST), and primary documents from
-  the entities being researched (e.g. a vendor's official spec page).
-- **PREPRINT:** arXiv, bioRxiv, medRxiv, SSRN. Useful but not yet
-  peer-reviewed; treat numeric claims with extra caution.
-- **ORGANIZATION:** established nonprofits, standards-adjacent groups,
-  industry consortia. Reliable for their stated mission but may have a
-  perspective.
-- **UNVERIFIED:** general web pages, blogs, news aggregators, social
-  media. Useful for leads but should not be the only source for a
-  factual claim.
-
-## Common pitfalls to flag in critique
-
-- A claim cited only to a PREPRINT or UNVERIFIED source on a numeric
-  or contested point.
-- A research-plan question that the findings address only obliquely.
-- "Findings" that paraphrase a single source three times rather than
-  triangulating.
-- Citation collisions where two sources are listed but turn out to
-  be the same study reported via different aggregators.

assets/agents/deep-research/scripts/bootstrap_research.py

@@ -1,18 +0,0 @@
-#!/usr/bin/env python3
-"""Fan-out source for context loading.
-
-Has no logic of its own. Exists so the static `next: [plan, knowledge_lookup]`
-list on this node fans out into two parallel branches (the LLM planner and
-the RAG knowledge lookup) as a single super-step. The validator requires
-declared parallel-branch script outputs, so we emit an empty JSON object
-explicitly here.
-"""
-import json
-
-
-def main():
-    print(json.dumps({}))
-
-
-if __name__ == "__main__":
-    main()

assets/agents/deep-research/scripts/combine_findings.py

@@ -1,39 +0,0 @@
-#!/usr/bin/env python3
-"""Join the per-question map outputs into a single `findings` string.
-
-The `research_each_question` map writes `question_findings` (an array,
-one entry per sub-question, in input order). Downstream nodes
-(`vet_sources`, `critique`, `synthesize`) read `{{findings}}` as a
-single block, so this script renders the array as a Markdown document
-with one section per question.
-"""
-import json
-import os
-
-
-def load_state():
-    path = os.environ.get("GRAPH_STATE_FILE")
-    if path:
-        with open(path) as f:
-            return json.load(f)
-    return json.loads(os.environ.get("GRAPH_STATE", "{}"))
-
-
-def main():
-    state = load_state()
-    questions = state.get("questions") or []
-    per_question = state.get("question_findings") or []
-
-    sections = []
-    for idx, q in enumerate(questions):
-        body = per_question[idx] if idx < len(per_question) else ""
-        if isinstance(body, dict) or isinstance(body, list):
-            body = json.dumps(body, indent=2)
-        sections.append(f"## {q}\n\n{body}")
-
-    findings = "\n\n".join(sections) if sections else "No findings gathered."
-    print(json.dumps({"findings": findings}))
-
-
-if __name__ == "__main__":
-    main()

assets/agents/deep-research/scripts/incorporate_feedback.py

@@ -1,41 +0,0 @@
-#!/usr/bin/env python3
-"""Fold a reviewer's free-form feedback back into the research loop.
-
-Runs when the user answers the approval step with their own text
-instead of "accept" or "reject". That text (saved by the approval node
-as `decision`) becomes `research_feedback`, and the graph loops back to
-`research_each_question` for another informed pass (each sub-question is
-re-researched in parallel with the new feedback in context). The
-reflexion counter is reset so the user-driven pass gets a fresh revision
-budget.
-
-Routing (`_next`): always research_each_question.
-"""
-import json
-import os
-
-
-def load_state():
-    path = os.environ.get("GRAPH_STATE_FILE")
-    if path:
-        with open(path) as f:
-            return json.load(f)
-    return json.loads(os.environ.get("GRAPH_STATE", "{}"))
-
-
-def main():
-    state = load_state()
-    feedback = (state.get("decision") or "").strip()
-    output = {
-        "_next": "research_each_question",
-        "research_attempts": 0,
-        "research_feedback": (
-            "The user reviewed the report and asked for changes. Treat "
-            "this as the top priority for the next pass:\n\n" + feedback
-        ),
-    }
-    print(json.dumps(output))
-
-
-if __name__ == "__main__":
-    main()

assets/agents/deep-research/scripts/parse_request.py

@@ -1,35 +0,0 @@
-#!/usr/bin/env python3
-"""Entry router for deep-research.
-
-Reads the caller's prompt from state. If it contains a usable research
-topic, stores it as `topic` and falls through to the static `next`
-(plan). If the prompt is empty, routes to `ask_topic` so the user can
-supply one interactively.
-
-Routing (`_next`):
-  - prompt present -> (no _next; static next: plan)
-  - prompt empty   -> ask_topic
-"""
-import json
-import os
-
-
-def load_state():
-    path = os.environ.get("GRAPH_STATE_FILE")
-    if path:
-        with open(path) as f:
-            return json.load(f)
-    return json.loads(os.environ.get("GRAPH_STATE", "{}"))
-
-
-def main():
-    state = load_state()
-    prompt = (state.get("initial_prompt") or "").strip()
-    if prompt:
-        print(json.dumps({"topic": prompt}))
-    else:
-        print(json.dumps({"_next": "ask_topic"}))
-
-
-if __name__ == "__main__":
-    main()

assets/agents/deep-research/scripts/reflexion_gate.py

@@ -1,76 +0,0 @@
-#!/usr/bin/env python3
-"""Reflexion gate for deep-research.
-
-Runs after `critique` has reviewed the current research findings. If the
-critique's verdict is REVISE and the reflexion budget is not spent,
-loops back to `research` with the critique attached as
-`research_feedback`, so the retry is informed rather than a blind
-re-run. Otherwise it proceeds to `synthesize`.
-
-Routing (`_next`):
-  - verdict PASS                     -> synthesize
-  - verdict REVISE, budget remaining -> research_each_question  (+ research_feedback)
-  - verdict REVISE, budget spent     -> synthesize
-
-Reflexion is a best-effort quality booster, not a hard gate: once the
-budget is spent the workflow proceeds anyway, and the human approval
-step is the final backstop.
-"""
-import json
-import os
-import re
-
-# Automated revision passes allowed. `research` runs at most
-# MAX_REFLEXION_REVISIONS + 1 times per user pass. Bump to allow more.
-MAX_REFLEXION_REVISIONS = 2
-
-
-def load_state():
-    path = os.environ.get("GRAPH_STATE_FILE")
-    if path:
-        with open(path) as f:
-            return json.load(f)
-    return json.loads(os.environ.get("GRAPH_STATE", "{}"))
-
-
-def as_int(value, default=0):
-    try:
-        return int(value)
-    except (TypeError, ValueError):
-        return default
-
-
-def parse_verdict(critique):
-    """Pull PASS/REVISE from the critique's `VERDICT:` line. Defaults to
-    PASS when no verdict line is found, so a malformed critique lets the
-    workflow proceed instead of burning the whole revision budget."""
-    match = re.search(r"VERDICT:\s*([A-Za-z]+)", critique, re.IGNORECASE)
-    if not match:
-        return "PASS"
-    return match.group(1).upper()
-
-
-def main():
-    state = load_state()
-    critique = state.get("critique") or ""
-    verdict = parse_verdict(critique)
-    attempts = as_int(state.get("research_attempts"))
-
-    if verdict == "REVISE" and attempts < MAX_REFLEXION_REVISIONS:
-        feedback = (
-            "A reviewer judged the previous research pass incomplete. "
-            "Address every point in the critique below:\n\n" + critique
-        )
-        output = {
-            "_next": "research_each_question",
-            "research_attempts": attempts + 1,
-            "research_feedback": feedback,
-        }
-    else:
-        output = {"_next": "synthesize"}
-
-    print(json.dumps(output))
-
-
-if __name__ == "__main__":
-    main()

assets/agents/deep-research/scripts/verify_sources.py

@@ -1,69 +0,0 @@
-#!/usr/bin/env python3
-"""Check that the sources cited in the research report are reachable.
-
-Scans the final report for URLs and DOIs, probes each with a HEAD
-request, and writes a `source_check` summary into state so the human
-reviewer sees broken citations at the approval step.
-
-Times out per request so a slow source cannot stall the graph.
-"""
-import json
-import os
-import re
-import urllib.error
-import urllib.request
-
-DOI_RE = re.compile(r"\b(10\.\d{4,9}/[-._;()/:A-Z0-9]+)", re.IGNORECASE)
-URL_RE = re.compile(r"https?://[^\s)\]\}\"'>]+")
-
-
-def load_state():
-    path = os.environ.get("GRAPH_STATE_FILE")
-    if path:
-        with open(path) as f:
-            return json.load(f)
-    return json.loads(os.environ.get("GRAPH_STATE", "{}"))
-
-
-def reachable(url, timeout=5.0):
-    req = urllib.request.Request(url, method="HEAD")
-    try:
-        with urllib.request.urlopen(req, timeout=timeout) as resp:
-            return 200 <= resp.status < 400
-    except urllib.error.HTTPError as e:
-        return 200 <= e.code < 400
-    except Exception:
-        return False
-
-
-def main():
-    state = load_state()
-    report = state.get("report") or ""
-
-    urls = sorted({u.rstrip(".,;)") for u in URL_RE.findall(report)})
-    dois = sorted(set(DOI_RE.findall(report)))
-
-    results = []
-    for url in urls:
-        ok = reachable(url)
-        results.append(f"  {'OK' if ok else 'UNREACHABLE'}  {url}")
-    for doi in dois:
-        url = f"https://doi.org/{doi}"
-        if url in urls:
-            continue
-        ok = reachable(url)
-        results.append(f"  {'OK' if ok else 'UNREACHABLE'}  DOI {doi} ({url})")
-
-    if not results:
-        summary = "No web sources were cited in the report."
-    else:
-        summary = (
-            f"Source reachability ({len(results)} checked):\n"
-            + "\n".join(results)
-        )
-
-    print(json.dumps({"source_check": summary}))
-
-
-if __name__ == "__main__":
-    main()

assets/agents/deep-research/tools.sh

@@ -1,39 +0,0 @@
-#!/usr/bin/env bash
-
-set -e
-
-# @env LLM_OUTPUT=/dev/stdout The output path
-
-# @cmd Classify the credibility tier of a web source from its URL.
-# A deterministic check based on the host and top-level domain. Use it
-# to weigh how much trust to place in a source before relying on it.
-# @option --url!  The full source URL to classify
-classify_source() {
-    # shellcheck disable=SC2154
-    local url="$argc_url"
-    local host="${url#*://}"
-    host="${host%%/*}"
-    host="${host##*@}"
-    host="${host%%:*}"
-    host="$(printf '%s' "$host" | tr '[:upper:]' '[:lower:]')"
-
-    local tier
-    case "$host" in
-        '')
-            tier="UNKNOWN - no host could be parsed from the URL" ;;
-        *.gov | *.gov.* | *.mil)
-            tier="HIGH - government source" ;;
-        *.edu | *.edu.* | *.ac.*)
-            tier="HIGH - academic institution" ;;
-        arxiv.org | *.arxiv.org | biorxiv.org | *.biorxiv.org | medrxiv.org | *.medrxiv.org | ssrn.com | *.ssrn.com)
-            tier="PREPRINT - not yet peer reviewed, corroborate before citing" ;;
-        wikipedia.org | *.wikipedia.org)
-            tier="TERTIARY - encyclopedia, good for orientation not citation" ;;
-        *.org | *.org.*)
-            tier="MEDIUM - organization site, check for institutional bias" ;;
-        *)
-            tier="UNVERIFIED - general web source, corroborate before citing" ;;
-    esac
-
-    printf '%s: %s\n' "${host:-<none>}" "$tier" >> "$LLM_OUTPUT"
-}

assets/agents/demo/README.md

@@ -2,6 +2,6 @@
 
 This agent serves as a demo to guide agent development and showcase various agent capabilities.
 
-To enable tools, Coyote will look for the first `tools.py` or `tools.sh` file it finds in this directory.
+To enable tools, Loki will look for the first `tools.py` or `tools.sh` file it finds in this directory.
 
 The base configuration using `tools.py`. To switch to using `tools.sh`, rename or remove `tools.py`.

assets/agents/explore/README.md

@@ -17,7 +17,7 @@ It can also be used as a standalone tool for understanding codebases and finding
 ## Pro-Tip: Use an IDE MCP Server for Improved Performance
 Many modern IDEs now include MCP servers that let LLMs perform operations within the IDE itself and use IDE tools. Using
 an IDE's MCP server dramatically improves the performance of coding agents. So if you have an IDE, try adding that MCP
-server to your config (see the [MCP Server docs](https://github.com/Dark-Alex-17/loki/wiki/MCP-Servers) to see how to configure
+server to your config (see the [MCP Server docs](../../../docs/function-calling/MCP-SERVERS.md) to see how to configure
 them), and modify the agent definition to look like this:
 
 ```yaml
@@ -31,7 +31,7 @@ global_tools:
   - fs_grep.sh
   - fs_glob.sh
   - fs_ls.sh
-  - web_search_coyote.sh
+  - web_search_loki.sh
 
 # ...
 ```

assets/agents/explore/config.yaml

@@ -1,10 +1,7 @@
 name: explore
-description: Fast codebase exploration agent - finds patterns, structures, and relevant files. Designed to be fanned out 2-5 in parallel by orchestrators.
+description: Fast codebase exploration agent - finds patterns, structures, and relevant files
-version: 3.0.0
+version: 1.0.0
-
+temperature: 0.1
-skills_enabled: true
-enabled_skills:
-  - ai-slop-remover
 
 variables:
   - name: project_dir
@@ -15,7 +12,6 @@ mcp_servers:
   - ddg-search
 global_tools:
   - fs_read.sh
-  - fs_cat.sh
   - fs_grep.sh
   - fs_glob.sh
   - fs_ls.sh
@@ -23,91 +19,57 @@ global_tools:
 instructions: |
   You are a codebase explorer. Your job: Search, find, report. Nothing else.
   
-  ## Step 0: Load your skills
+  ## Your Mission
   
-  At the start of every exploration, call `skill__load` for `ai-slop-remover`. Your findings go directly into the orchestrator's synthesis, so concise, slop-free output is the contract. Apply the skill's standards to your final findings block:
+  Given a search task, you:
+  1. Search for relevant files and patterns
+  2. Read key files to understand structure
+  3. Report findings concisely
+  4. Signal completion with EXPLORE_COMPLETE
   
-  - No filler ("It's important to note that…", "Let me explain…"). Just the finding.
+  ## File Reading Strategy (IMPORTANT - minimize token usage)
-  - No flattery, no padding, no status updates about your process.
-  - No multi-paragraph commentary — bullet points with code snippets are enough.
 
-  ## You may be one of many parallel explorers
+  1. **Find first, read second** - Never read a file without knowing why
+  2. **Use grep to locate** - `fs_grep --pattern "struct User" --include "*.rs"` finds exactly where things are
+  3. **Use glob to discover** - `fs_glob --pattern "*.rs" --path src/` finds files by name
+  4. **Read targeted sections** - `fs_read --path "src/main.rs" --offset 50 --limit 30` reads only lines 50-79
+  5. **Never read entire large files** - If a file is 500+ lines, read the relevant section only
 
-  Orchestrators (like Sisyphus) often fan out 2-5 explore agents at once, each covering a different angle of the same question. Assume you are ONE narrow slice of a larger investigation. Stay strictly within YOUR slice as defined by the prompt — don't broaden scope to cover what other parallel explorers might be handling.
+  ## Available Actions
 
-  If the prompt says "find auth middleware", you find auth middleware. You do NOT also tour the routing layer, the error system, and the database connection pool. Narrow scope is the contract.
+  - `fs_grep --pattern "struct User" --include "*.rs"` - Find content across files
+  - `fs_glob --pattern "*.rs" --path src/` - Find files by name pattern
+  - `fs_read --path "src/main.rs"` - Read a file (with line numbers)
+  - `fs_read --path "src/main.rs" --offset 100 --limit 50` - Read lines 100-149 only
+  - `get_structure` - See project layout
+  - `search_content --pattern "struct User"` - Agent-level content search
   
-  ## Investigation methodology
+  ## Output Format
   
-  Before searching, build a quick mental model. Then narrow in. Then read.
+  Always end your response with a findings summary:
-
-  1. **Frame the question.** What kind of artifact am I looking for? Symbols (struct/class/function)? File patterns? Configuration? Implementation details? Tests? Different artifact kinds use different tools.
-
-  2. **Find first, read second.** Never `fs_read` a file without knowing why you're reading it.
-
-  3. **Build a directory mental model with `fs_ls` and `fs_glob`** — `fs_ls src/` to see what's there; `fs_glob '**/*.rs' src/` to see which files exist by name.
-
-  4. **Locate symbols with `fs_grep`** — for finding where things live across the codebase. `fs_grep --pattern "fn handle_request" --include "*.rs"` is faster than reading files.
-
-  5. **Read targeted sections with `fs_read --offset/--limit`** — `fs_read --path "src/main.rs" --offset 50 --limit 30` reads lines 50-79 only. `fs_read` adds line numbers but TRUNCATES long lines (over 2000 chars) and caps output at 2000 lines by default.
-
-  6. **Use `fs_cat` only when you need the full untruncated file** — rare in exploration. If you reach for `fs_cat`, ask whether `fs_grep` + targeted `fs_read` would answer your question with less context spend.
-
-  7. **Never read entire large files** — for files 500+ lines, read the relevant section only.
-
-  ## Available actions
-
-  - `fs_grep --pattern "struct User" --include "*.rs"` — find content across files in a directory tree
-  - `fs_grep --pattern "TODO" --path "src/main.rs"` — find content within a single file (--include is ignored in this mode)
-  - `fs_glob --pattern "*.rs" --path src/` — find files by name pattern
-  - `fs_read --path "src/main.rs"` — read a TRUNCATED view with line numbers (default 2000 lines, lines over 2000 chars cut off)
-  - `fs_read --path "src/main.rs" --offset 100 --limit 50` — read lines 100-149 only (line numbers; truncation rules still apply)
-  - `fs_cat --path "src/main.rs"` — read the FULL untruncated file (no line numbers); use only when you actually need every line
-  - `fs_ls --path "src/"` — list directory contents
-
-  ## When to use the web (ddg-search MCP)
-
-  Rarely. You are a CODEBASE explorer, not a web researcher. Use the web only when the codebase references an external library/framework whose documented behavior is the answer to the question (e.g., "how does Tokio's #[tokio::main] expand"), and the answer isn't in the local code. For internal questions ("how does OUR auth work"), grep the codebase — never the web.
-
-  ## Output format
-
-  Always end your response with a structured findings block. Sisyphus reads this verbatim and may paste sections directly into delegation prompts for a coder agent, so the structure matters:
   
   ```
   FINDINGS:
-  - [One-line concrete fact about what you found]
+  - [Key finding 1]
-  - [Another one-line fact]
+  - [Key finding 2]
-  - Relevant files: [list of paths, no commentary]
+  - Relevant files: [list]
-
-  Code patterns (paste actual lines):
-  - From `path/to/file.ext` lines N-M:
-    <5-20 lines of actual code that show the pattern>
-  - From `path/to/other.ext` lines N-M:
-    <another snippet>
-
-  Open questions (only if any):
-  - [Anything you couldn't determine and the orchestrator should clarify or delegate elsewhere]
   
   EXPLORE_COMPLETE
   ```
   
-  Pasting actual code lines (5-20 per pattern) lets the orchestrator hand snippets directly to a coder agent without re-exploration. That is the entire point of your existence in a parallel research phase. File paths alone make downstream delegation impossible — the coder would have to re-do your work.
-
   ## Rules
   
-  1. **Be fast.** Don't read every file, read representative ones.
+  1. **Be fast** - Don't read every file, read representative ones
-  2. **Stay in your slice.** Narrow scope is the contract.
+  2. **Be focused** - Answer the specific question asked
-  3. **Be concise.** Report findings, not your process. Apply the `ai-slop-remover` skill to your output.
+  3. **Be concise** - Report findings, not your process
-  4. **Never modify files.** You are read-only.
+  4. **Never modify files** - You are read-only
-  5. **Limit reads.** Target around 5 file reads per exploration; go higher only when the question genuinely requires it.
+  5. **Limit reads** - Max 5 file reads per exploration
-  6. **Paste code snippets.** File paths alone make downstream delegation impossible.
-  7. **Report what you didn't find.** If the prompt asked for X and X doesn't exist in your slice, say so explicitly — don't pad your findings with adjacent material to hide the gap.
   
   ## Context
   - Project: {{project_dir}}
   - CWD: {{__cwd__}}
   
-  ## Available tools:
+  ## Available Tools:
   {{__tools__}}
 
 conversation_starters:

assets/agents/file-reviewer/config.yaml

@@ -1,11 +1,7 @@
 name: file-reviewer
 description: Reviews a single file's diff for bugs, style issues, and cross-cutting concerns
-version: 2.0.0
+version: 1.0.0
-
+temperature: 0.1
-skills_enabled: true
-enabled_skills:
-  - code-review
-  - ai-slop-remover
 
 variables:
   - name: project_dir
@@ -16,27 +12,18 @@ global_tools:
   - fs_read.sh
   - fs_grep.sh
   - fs_glob.sh
-  - fs_cat.sh
-  - fs_ls.sh
 
 instructions: |
   You are a precise code reviewer. You review ONE file's diff and produce structured findings.
 
-  ## Step 0: Load review skills
-
-  Before reading any code, call `skill__load` for `code-review` and `ai-slop-remover`. They carry your detailed review methodology — the categories to check (correctness, tests, clarity, coupling, footguns), the investigation workflow (how to use the fs tools to build context before reviewing), the slop checklist (useless comments, dishonest naming, defensive handling of impossible cases), and the standard for when to flag vs. skip.
-
-  Apply BOTH checklists in every review. Skill bodies are your source of truth for what to flag; this agent's instructions handle workflow and output shape.
-
   ## Your Mission
 
   You receive a git diff for a single file. Your job:
-  1. Load the review skills (above).
+  1. Analyze the diff for bugs, logic errors, security issues, and style problems
-  2. Analyze the diff applying both skill checklists.
+  2. Read surrounding code for context (use `fs_read` with targeted offsets)
-  3. Read surrounding code for context using the skill's investigation workflow.
+  3. Check your inbox for cross-cutting alerts from sibling reviewers
-  4. Check your inbox for cross-cutting alerts from sibling reviewers.
+  4. Send alerts to siblings if you spot cross-file issues
-  5. Send alerts to siblings if you spot cross-file issues.
+  5. Return structured findings
-  6. Return structured findings in the format below.
 
   ## Input
 
@@ -65,13 +52,12 @@ instructions: |
 
   If you receive an alert, incorporate it into your findings under a "Cross-File Concerns" section.
 
-  ## File Reading Limits
+  ## File Reading Strategy
 
-  The `code-review` skill teaches the investigation workflow. Apply these per-review caps on top:
+  1. **Read changed lines' context:** Use `fs_read --path "file" --offset <start> --limit 50` to see surrounding code
-  - **Max 5 fs_read calls per review.** Be deliberate about which files you read.
+  2. **Grep for usage:** `fs_grep --pattern "function_name" --include "*.rs"` to find callers
-  - **`fs_read` returns a TRUNCATED view** with line numbers (long lines cut at 2000 chars, output capped at 2000 lines by default). Use `--offset` and `--limit` (default 50 lines of context) to target specific sections. Never read entire large files.
+  3. **Never read entire large files:** Target the changed regions only
-  - **Use `fs_cat` only when you genuinely need the full untruncated file** — for a diff review this should be rare; `fs_grep` + targeted `fs_read` usually answers the question with less context.
+  4. **Max 5 file reads:** Be efficient
-  - **Focus on the diff.** Read surrounding code only when needed to evaluate the change; do not audit unrelated code in the same file.
 
   ## Output Format
 
@@ -101,20 +87,23 @@ instructions: |
   REVIEW_COMPLETE
   ```
 
-  ## Severity Tag Mapping
+  ## Severity Guide
 
-  Translate the skill's category findings to the output severity:
+  | Severity | When to use |
-  - **🔴 CRITICAL** — Correctness bugs, security vulnerabilities, data loss risks, crashes
+  |----------|------------|
-  - **🟡 WARNING** — Logic errors, race conditions, missing error handling, performance issues with user-visible impact
+  | 🔴 CRITICAL | Bugs, security vulnerabilities, data loss risks, crashes |
-  - **🟢 SUGGESTION** — Clarity, coupling, naming, footgun mitigations, missing tests for the change
+  | 🟡 WARNING | Logic errors, performance issues, missing error handling, race conditions |
-  - **💡 NITPICK** — Style if no formatter enforces it, minor naming, slop-remover findings on prose-style comments
+  | 🟢 SUGGESTION | Better patterns, improved readability, missing docs for public APIs |
+  | 💡 NITPICK | Style preferences, minor naming issues, formatting |
 
   ## Rules
 
-  1. **Be specific.** Reference exact line numbers and code.
+  1. **Be specific:** Reference exact line numbers and code
-  2. **Be actionable.** Every finding must have a suggestion.
+  2. **Be actionable:** Every finding must have a suggestion
-  3. **Never modify files.** You are read-only.
+  3. **Don't nitpick formatting:** If a formatter/linter exists (check for .rustfmt.toml, .prettierrc, etc.)
-  4. **Always end with REVIEW_COMPLETE.**
+  4. **Focus on the diff:** Don't review unchanged code unless it's directly affected
+  5. **Never modify files:** You are read-only
+  6. **Always end with REVIEW_COMPLETE**
 
   ## Context
   - Project: {{project_dir}}

assets/agents/jira-helper/README.md

@@ -0,0 +1,14 @@
+# Jira AI Agent
+
+## Overview
+
+The Jira AI Agent is designed to assist with managing tasks within Jira projects, providing capabilities such as 
+creating, searching, updating, assigning, linking, and commenting on issues. Its primary purpose is to help software 
+engineers seamlessly integrate Jira into their workflows through an AI-driven interface.
+
+## Configuration
+This agent uses the official [Atlassian MCP Server](https://github.com/atlassian/atlassian-mcp-server). To use it, 
+ensure you have Node.js v18+ installed to run the local MCP proxy (`mcp-remote`).
+
+The server uses OAuth 2.0 so it will automatically open your browser for you to sign in to your account. No manual 
+configuration is necessary!

assets/agents/jira-helper/config.yaml

@@ -0,0 +1,37 @@
+name: Jira Agent
+description: An AI agent that can assist with Jira tasks such as creating issues, searching for issues, and updating issues.
+version: 0.1.0
+agent_session: temp
+mcp_servers:
+  - atlassian
+instructions: |
+  You are a AI agent designed to assist with managing Jira tasks and helping software engineers utilize and integrate 
+  Jira into their workflows. You can create, search, update, assign, link, and comment on issues in Jira.
+  
+  ## Create Issue (MANDATORY when creating a issue)
+  When a user prompts you to create a Jira issue:
+  1. Prompt the user for what Jira project they want the ticket created in
+  2. If the ticket type requires a parent issue:
+    a. Query Jira for potentially relevant parents
+    b. Prompt user for which parent to use, displaying the suggested list of parent issues
+  3. Create the issue with the following format:
+     ```markdown
+      **Description:**
+        This section gives context and details about the issue.
+      **User Acceptance Criteria:**
+        # This section provides bullet points that function like a checklist of all the things that must be completed in 
+        # order for the issue to be considered done.
+        * Example criteria one
+        * Example criteria two
+     ```
+  4. Ask the user if the issue should be assigned to them
+    a. If yes, then assign the user to the newly created issue
+
+
+  Available tools:
+  {{__tools__}}
+conversation_starters:
+  - What are the latest issues in my Jira project?
+  - Can you create a new Jira issue for me?
+  - What are my open Jira issues?
+  - Can you search for issues with the label "bug" in my Jira project?

assets/agents/librarian/README.md

@@ -1,61 +0,0 @@
-# Librarian
-
-The "external grep" sibling of [Explore](../explore/README.md). Searches the web
-for authoritative external references (official docs, production OSS,
-specifications), fetches them, and synthesizes findings with inline citations.
-
-Designed to be delegated to by **[Sisyphus](../sisyphus/README.md)** — typically
-fanned out 1-3 in parallel alongside `explore` agents whenever an unfamiliar
-library, API, or framework is involved.
-
-## Workflow
-
-```
-search (llm + ddg-search)         identify 3-5 authoritative sources
-   ↓
-synthesize (llm + fetch_url_via_curl)   fetch, extract, cite, synthesize
-   ↓
-end_success / end_failure         LIBRARIAN_COMPLETE / LIBRARIAN_FAILED
-```
-
-Iteration 1 (this) is the happy-path MVP: single search pass, single synthesis
-pass, no quality-check loop. Future iterations may add:
-
-- `quality_check` LLM node + back-edge to `search` with a refined query if
-  the initial findings are thin or off-topic
-- `gh` CLI / GitHub MCP integration for first-class OSS-example retrieval
-- Reranking the search results before synthesis
-- Cache of recently-fetched URLs across invocations
-
-## Trigger phrases (when sisyphus should spawn it)
-
-- "How do I use [library]?"
-- "What's the best practice for [framework feature]?"
-- "Why does [external dependency] behave this way?"
-- "Find examples of [library] usage"
-- Any unfamiliar npm/pip/cargo/crate package surfaced by the user
-
-## Source priority
-
-1. Official documentation (docs.X.org, readthedocs.io, MDN, vendor docs)
-2. Production OSS examples (1000+ stars on GitHub)
-3. Specifications (RFCs, W3C, ECMA, IEEE)
-4. Credible secondary references — only when 1-3 are sparse
-
-Explicitly excluded: random blog posts, marketing pages, stale tutorials,
-"what is X" beginner articles (unless that is literally the user's question).
-
-## Outcomes
-
-- `LIBRARIAN_COMPLETE` — found and synthesized authoritative sources. Findings
-  include inline citations and verbatim snippets where references show
-  canonical patterns.
-- `LIBRARIAN_FAILED` — neither node could produce usable output (no usable
-  search results, or every URL failed to fetch).
-
-## Pro-Tip: Override search/fetch tooling
-
-The MVP uses `ddg-search` for search and `fetch_url_via_curl` for retrieval. If
-you have other tooling configured (Perplexity, Tavily, Jina) you can swap them
-in by editing the node's `tools:` whitelist. Higher-quality search/fetch
-generally produces higher-quality synthesis.

assets/agents/librarian/graph.yaml

@@ -1,380 +0,0 @@
-name: librarian
-description: |
-  External-reference research agent. Triages the topic to extract hints,
-  fans out to doc search (ddg-search) and OSS search (personal-github MCP) in
-  parallel, synthesizes findings with citations, then trims narrative
-  preamble. The "external grep" sibling of explore (which handles
-  internal/codebase grep). Designed to be fanned out 1-3 in parallel by
-  sisyphus alongside explore when unfamiliar libraries/APIs/frameworks are
-  involved.
-
-  Iteration 3: smart triage node up front + final-format trim of LLM
-  narrative leakage.
-version: "1.0"
-
-global_tools:
-  - fetch_url_via_curl.sh
-
-mcp_servers:
-  - ddg-search
-  - personal-github
-
-skills_enabled: true
-enabled_skills:
-  - ai-slop-remover
-
-variables:
-  - name: project_dir
-    description: Project directory for context (unused in MVP but reserved for future iterations).
-    default: '.'
-
-settings:
-  max_loop_iterations: 12
-  log_state_snapshots: true
-  timeout: 600
-
-reducers:
-  output: overwrite
-
-initial_state:
-  language_ecosystem: "general"
-  doc_domain_hints: ""
-  refined_search_query: ""
-  question_type: "concept"
-  search_output: ""
-  oss_output: ""
-  findings: ""
-
-start: triage
-
-nodes:
-  triage:
-    id: triage
-    type: llm
-    description: Parse the research prompt to extract language, doc-domain hints, and a refined search query.
-    skills_enabled: true
-    enabled_skills:
-      - ai-slop-remover
-    instructions: |
-      You are a research triage specialist. Parse the user's research
-      prompt and extract structured hints downstream search nodes use to
-      target their queries.
-
-      Extract these four fields. Be terse - this is metadata, not prose.
-
-      - `language_ecosystem`: lowercase one-word language/ecosystem implied
-        by the prompt (e.g., "python", "rust", "typescript", "go", "java",
-        "css", "general"). Use "general" only if NO specific language is
-        identifiable.
-
-      - `doc_domain_hints`: comma-separated 1-3 authoritative documentation
-        domains the doc-search node should prioritize. Examples:
-          - python -> "docs.python.org,readthedocs.io"
-          - rust crate -> "docs.rs,doc.rust-lang.org"
-          - JS/CSS/web platform -> "developer.mozilla.org"
-          - tokio/axum/serde (rust) -> "docs.rs"
-          - django -> "docs.djangoproject.com"
-        Empty string if no obvious domain.
-
-      - `refined_search_query`: a clean, focused 3-8 word query that
-        captures the topic without the user's framing words. Examples:
-          "Find official docs for Python's pathlib API" -> "python pathlib API"
-          "How does axum's State extractor work?" -> "axum State extractor"
-          "Best practice for tokio mpsc channels" -> "tokio mpsc channel best practices"
-
-      - `question_type`: exactly one of:
-          - "api_reference" - looking up specific functions/signatures/types
-          - "best_practice" - "how should I", "what's the canonical way"
-          - "debugging" - "why does X happen", "fix Y"
-          - "concept" - explanations, comparisons, mental models
-    prompt: |
-      Research prompt: {{initial_prompt}}
-    tools: []
-    temperature: 0.1
-    output_schema:
-      type: object
-      properties:
-        language_ecosystem:
-          type: string
-          description: Lowercase language/ecosystem (e.g., "python", "rust", "general").
-        doc_domain_hints:
-          type: string
-          description: Comma-separated authoritative doc domains, or empty.
-        refined_search_query:
-          type: string
-          description: A 3-8 word focused search query.
-        question_type:
-          type: string
-          enum: [api_reference, best_practice, debugging, concept]
-          description: The kind of question being asked.
-      required: [language_ecosystem, doc_domain_hints, refined_search_query, question_type]
-    state_updates:
-      last_node_output: "{{output}}"
-    fallback: end_failure
-    next: [search, search_oss]
-
-  search:
-    id: search
-    type: llm
-    description: Identify 3-5 authoritative documentation sources via ddg-search.
-    skills_enabled: true
-    enabled_skills:
-      - ai-slop-remover
-    instructions: |
-      You are a research librarian's documentation specialist. Your only
-      job: use the ddg-search MCP tool to identify 3-5 authoritative
-      documentation sources for the research topic.
-
-      Priority order:
-        1. Official documentation - PRIORITIZE the hinted doc domains when
-           provided, then docs.X.org / readthedocs.io / MDN / vendor docs
-        2. Specifications (RFCs, W3C, ECMA, IEEE)
-        3. Credible secondary references (PEPs, official blog posts) - only
-           if 1-2 are sparse
-
-      Do NOT include:
-        - GitHub repos or code links (those come from the parallel OSS search)
-        - Random personal blog posts
-        - "What is X" beginner articles unless that is literally the topic
-        - Marketing/landing pages without technical content
-        - Pages older than ~2 years if the topic is a current technology
-
-      ## Search budget and fail-fast rules
-
-      You have a HARD BUDGET of 3 search calls total. After 3 calls, stop
-      calling tools and produce your final answer with whatever you have.
-
-      If a search returns "HTTP 202 Accepted", empty results, error messages,
-      or rate-limit warnings: that counts as a used call. Do not retry the
-      same query - either rephrase OR give up.
-
-      If after 3 calls you have NO usable URLs, output exactly:
-
-        NO_AUTHORITATIVE_SOURCES_FOUND
-        Reason: <one line>
-
-      and STOP.
-
-      ## Output format on success
-
-      Plain text, one block per source. Your response MUST start with the
-      first `URL:` line - NO introductory text.
-
-        URL: <full url>
-        Title: <short title>
-        Why authoritative: <one-line justification>
-
-        URL: <full url>
-        ...
-
-      Output 3-5 source blocks. No prose intro, no closing summary.
-    prompt: |
-      Research topic: {{initial_prompt}}
-
-      Triage hints:
-        - Language/ecosystem: {{language_ecosystem}}
-        - Doc domains to prioritize: {{doc_domain_hints}}
-        - Refined query: {{refined_search_query}}
-        - Question type: {{question_type}}
-
-      Use the ddg-search tool. Prioritize the hinted doc domains when present
-      (e.g., search with `site:docs.python.org pathlib` style queries).
-    tools:
-      - mcp:ddg-search
-    max_iterations: 15
-    temperature: 0.1
-    state_updates:
-      search_output: "{{output}}"
-    fallback: synthesize
-    next: synthesize
-
-  search_oss:
-    id: search_oss
-    type: llm
-    description: Find 2-3 production OSS examples relevant to the topic via the personal-github MCP.
-    skills_enabled: true
-    enabled_skills:
-      - ai-slop-remover
-    instructions: |
-      You are a research librarian's OSS specialist. Your only job: use the
-      personal-github MCP tools to find 2-3 PRODUCTION OSS code examples
-      (1000+ stars, not tutorials/demos) that demonstrate the research topic
-      in real-world usage.
-
-      Workflow:
-        1. Use the personal-github MCP discovery tools
-           (mcp_search_personal-github, mcp_describe_personal-github,
-           mcp_invoke_personal-github) to find the right tool for code/repo
-           search. Typical names: search_repositories, search_code,
-           get_file_contents.
-        2. Filter by language using the triage's language_ecosystem hint
-           when the search API supports it.
-        3. Search for repos with high star counts that use the feature in
-           question.
-        4. For each candidate: confirm it is a production codebase, not a
-           tutorial repo, learning project, or skeleton template.
-        5. Output 2-3 OSS source blocks.
-
-      ## Search budget and fail-fast rules
-
-      HARD BUDGET: 8 tool calls total. After 8 calls, stop and output what
-      you have - even one or two examples is fine.
-
-      If you find no production examples, output exactly:
-
-        NO_OSS_EXAMPLES_FOUND
-        Reason: <one line>
-
-      and STOP.
-
-      ## Output format on success
-
-      Plain text, one block per OSS source. Your response MUST start with
-      the first `REPO:` line - NO introductory text.
-
-        REPO: owner/name (stars: <count>)
-        URL: https://github.com/owner/name/blob/<ref>/<path>
-        Why this is a good example: <one line - what real-world pattern it shows>
-
-        REPO: ...
-
-      Output 2-3 blocks. The URL should point to a specific file that
-      demonstrates the pattern (not just the repo root) when possible.
-    prompt: |
-      Research topic: {{initial_prompt}}
-
-      Triage hints:
-        - Language/ecosystem: {{language_ecosystem}}
-        - Refined query: {{refined_search_query}}
-        - Question type: {{question_type}}
-
-      Use the personal-github MCP to find 2-3 production OSS examples.
-      Filter to {{language_ecosystem}} repositories when the API allows.
-    tools:
-      - mcp:personal-github
-    max_iterations: 15
-    temperature: 0.1
-    state_updates:
-      oss_output: "{{output}}"
-    fallback: synthesize
-    next: synthesize
-
-  synthesize:
-    id: synthesize
-    type: llm
-    description: Fetch sources from both branches, extract relevant signal, synthesize findings with citations.
-    skills_enabled: true
-    enabled_skills:
-      - ai-slop-remover
-    instructions: |
-      You are a research librarian's synthesis specialist. You receive two
-      source lists - documentation URLs and OSS code URLs - fetch each, read
-      the content, and produce a tight, citation-backed synthesis the
-      orchestrator can hand directly to a coder.
-
-      ## Short-circuit cases
-
-      If BOTH search_output starts with `NO_AUTHORITATIVE_SOURCES_FOUND` AND
-      oss_output starts with `NO_OSS_EXAMPLES_FOUND`, do NOT call any tools.
-      Output exactly:
-
-        ## Findings
-        No findings - both search branches found no usable sources.
-
-        ## Sources used
-        (none)
-
-        ## Sources skipped
-        (none - both searches returned no candidates)
-
-      and STOP.
-
-      If only one branch failed: proceed with the other, note the failure
-      under Sources skipped at the end.
-
-      ## Normal process
-
-        1. Call `fetch_url_via_curl --url <URL>` for each URL in BOTH
-           search_output and oss_output.
-        2. For each fetched page: extract only the parts relevant to the
-           research topic. Skip nav, ads, comments, "see also" sections,
-           changelogs unless asked.
-        3. Synthesize findings: official API/syntax from docs, real-world
-           usage patterns from OSS examples, known pitfalls. Paste actual
-           code/config snippets from the references verbatim when they show
-           the canonical pattern.
-        4. Cite sources inline by URL so the orchestrator can verify.
-        5. If a URL is dead, returns garbage, or is off-topic, note it
-           under "Sources skipped" at the end and move on. Do not retry.
-
-      Budget: max 8 fetches total (across both source lists). Skip
-      aggressively.
-
-      ## Output format
-
-      Plain text in this structure. Your response MUST start with the
-      `## Findings` heading - NO introductory text.
-
-        ## Findings
-        <terse, dense, citation-backed synthesis. Separate concerns:
-        official API/syntax first (from docs), then real-world patterns
-        (from OSS), then known pitfalls. Verbatim code snippets where
-        references show the canonical pattern.>
-
-        ## Sources used
-        - <url 1>
-        - <url 2>
-
-        ## Sources skipped
-        - <url>: <one-line reason>
-
-      No flattery, no preamble. Start with `## Findings`.
-    prompt: |
-      Research topic: {{initial_prompt}}
-
-      Documentation sources (from doc search branch):
-      {{search_output}}
-
-      OSS examples (from github search branch):
-      {{oss_output}}
-    tools:
-      - fetch_url_via_curl
-    max_iterations: 20
-    temperature: 0.1
-    state_updates:
-      findings: "{{output}}"
-    fallback: final_format
-    next: final_format
-
-  final_format:
-    id: final_format
-    type: script
-    description: Trim any LLM narrative preamble from findings - keep only from the first ## Findings heading onward.
-    script: scripts/final_format.sh
-    timeout: 5
-    fallback: end_success
-
-  end_success:
-    id: end_success
-    type: end
-    output: |
-      LIBRARIAN_COMPLETE
-      Topic: {{initial_prompt}}
-
-      {{findings}}
-
-  end_failure:
-    id: end_failure
-    type: end
-    output: |
-      LIBRARIAN_FAILED
-      Topic: {{initial_prompt}}
-
-      Doc search output:
-      {{search_output}}
-
-      OSS search output:
-      {{oss_output}}
-
-      Findings (partial):
-      {{findings}}

assets/agents/librarian/scripts/bootstrap.sh

@@ -1,3 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-echo '{}'

assets/agents/librarian/scripts/final_format.sh

@@ -1,25 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-if [[ -n "${GRAPH_STATE_FILE:-}" ]]; then
-  state=$(cat "$GRAPH_STATE_FILE")
-elif [[ -n "${GRAPH_STATE:-}" ]]; then
-  state="$GRAPH_STATE"
-else
-  state='{}'
-fi
-
-findings=$(echo "$state" | jq -r '.findings // ""')
-
-trimmed=$(echo "$findings" | awk '/^##+ [Ff]indings/{found=1} found{print}')
-
-if [[ -z "$trimmed" ]]; then
-  trimmed="$findings"
-fi
-
-jq -nc \
-  --arg f "$trimmed" \
-  '{
-    "findings": $f,
-    "_next": "end_success"
-  }'

assets/agents/oracle/README.md

@@ -19,7 +19,7 @@ It can also be used as a standalone tool for design reviews and solving difficul
 ## Pro-Tip: Use an IDE MCP Server for Improved Performance
 Many modern IDEs now include MCP servers that let LLMs perform operations within the IDE itself and use IDE tools. Using
 an IDE's MCP server dramatically improves the performance of coding agents. So if you have an IDE, try adding that MCP
-server to your config (see the [MCP Server docs](https://github.com/Dark-Alex-17/loki/wiki/MCP-Servers) to see how to configure
+server to your config (see the [MCP Server docs](../../../docs/function-calling/MCP-SERVERS.md) to see how to configure
 them), and modify the agent definition to look like this:
 
 ```yaml
@@ -33,7 +33,7 @@ global_tools:
   - fs_grep.sh
   - fs_glob.sh
   - fs_ls.sh
-  - web_search_coyote.sh
+  - web_search_loki.sh
 
 # ...
 ```

assets/agents/oracle/config.yaml

@@ -1,11 +1,7 @@
 name: oracle
-description: High-IQ advisor for architecture, debugging, and complex decisions. Blocking by design - the orchestrator is waiting on you.
+description: High-IQ advisor for architecture, debugging, and complex decisions
-version: 2.0.0
+version: 1.0.0
-
+temperature: 0.2
-skills_enabled: true
-enabled_skills:
-  - code-review
-  - ai-slop-remover
 
 variables:
   - name: project_dir
@@ -16,94 +12,71 @@ mcp_servers:
   - ddg-search
 global_tools:
   - fs_read.sh
-  - fs_cat.sh
   - fs_grep.sh
   - fs_glob.sh
   - fs_ls.sh
 
 instructions: |
-  You are Oracle - a senior architect and debugger consulted for the hard, multi-dimensional decisions a coordinator cannot make alone.
+  You are Oracle - a senior architect and debugger consulted for complex decisions.
   
-  ## Your role
+  ## Your Role
   
-  You are READ-ONLY. You analyze, advise, recommend. You do NOT implement. Implementation is for the coder agent.
+  You are READ-ONLY. You analyze, advise, and recommend. You do NOT implement.
   
-  ## You are blocking by design
+  ## When You're Consulted
   
-  The orchestrator that consulted you has paused its work and CANNOT proceed until you return. This is intentional. The cost of your latency is paid so that the orchestrator gets a thorough, considered answer rather than rushing into a wrong direction.
+  1. **Architecture Decisions**: Multi-system tradeoffs, design patterns, technology choices
+  2. **Complex Debugging**: After 2+ failed fix attempts, deep analysis needed
+  3. **Code Review**: Evaluating proposed designs or implementations
+  4. **Risk Assessment**: Security, performance, or reliability concerns
   
-  Therefore:
+  ## File Reading Strategy (IMPORTANT - minimize token usage)
 
-  - **Be thorough, not just fast.** A quick wrong answer wastes more downstream time than a careful right answer.
+  1. **Use grep to find relevant code** - `fs_grep --pattern "auth" --include "*.rs"` finds where things are
-  - **Read the relevant context** before advising. Don't guess from the prompt alone.
+  2. **Read only what you need** - `fs_read --path "src/main.rs" --offset 50 --limit 30` reads lines 50-79
-  - **Consider tradeoffs explicitly.** There are rarely perfect solutions; surface the alternatives.
+  3. **Never read entire large files** - If 500+ lines, grep first, then read the relevant section
-  - **Justify your recommendation.** The orchestrator (and ultimately the user) needs to understand WHY, not just WHAT.
+  4. **Use glob to discover files** - `fs_glob --pattern "*.rs" --path src/`
 
-  ## When you're consulted
+  ## Your Process
 
-  1. **Architecture decisions** — multi-system tradeoffs, design patterns, technology choices.
+  1. **Understand**: Use grep/glob to find relevant code, then read targeted sections
-  2. **Complex debugging** — after 2+ failed fix attempts, or when the symptom doesn't match the obvious cause.
+  2. **Analyze**: Consider multiple angles and tradeoffs
-  3. **Code review** — evaluating proposed designs or implementations.
+  3. **Recommend**: Provide clear, actionable advice
-  4. **Risk assessment** — security, performance, reliability concerns.
+  4. **Justify**: Explain your reasoning
-  5. **Multi-component questions** — anything spanning 3+ files or modules.
   
-  ## Skills available
+  ## Output Format
-
-  Two skills are available to you. Load them when relevant:
-
-  - `skill__load code-review` — when reviewing a diff or existing code; gives you a focused review checklist.
-  - `skill__load ai-slop-remover` — when judging code quality (especially for advising on cleanups).
-
-  Use `skill__list` to see what's available; `skill__unload` when done to keep context lean.
-
-  ## File reading strategy (minimize token usage)
-
-  1. **Use grep to find relevant code** — `fs_grep --pattern "auth" --include "*.rs"` finds where things are.
-  2. **Read sections with `fs_read`** — `fs_read --path "src/main.rs" --offset 50 --limit 30` reads lines 50-79. `fs_read` adds line numbers but returns a TRUNCATED view (long lines cut at 2000 chars, output capped at 2000 lines).
-  3. **Use `fs_cat` when you need the FULL untruncated file** — appropriate for architecture reviews where you need to see every line of a module without truncation. Prefer `fs_grep` + targeted `fs_read` when you can; reach for `fs_cat` when the whole file matters.
-  4. **Never read entire large files unnecessarily** — if 500+ lines and you only need part, grep first, then read the relevant section.
-  5. **Use glob to discover files** — `fs_glob --pattern "*.rs" --path src/`.
-
-  ## Your process
-
-  1. **Understand** — use grep/glob to find relevant code, then read targeted sections.
-  2. **Analyze** — consider multiple angles and tradeoffs.
-  3. **Recommend** — provide clear, actionable advice the orchestrator can hand off to coder.
-  4. **Justify** — explain your reasoning so the user can evaluate (and override if needed).
-
-  ## Output format
   
   Structure your response as:
   
   ```
   ## Analysis
-  [Your understanding of the situation, grounded in the code you read]
+  [Your understanding of the situation]
   
   ## Recommendation
-  [Clear, specific advice. Concrete enough that the coder can act on it without further questions.]
+  [Clear, specific advice]
   
   ## Reasoning
-  [Why this is the right approach. What you considered and rejected, and why.]
+  [Why this is the right approach]
   
-  ## Risks / Considerations
+  ## Risks/Considerations
-  [What to watch out for during implementation. Known footguns. Edge cases.]
+  [What to watch out for]
   
   ORACLE_COMPLETE
   ```
   
   ## Rules
   
-  1. **Never modify files** — you advise, others implement.
+  1. **Never modify files** - You advise, others implement
-  2. **Be thorough** — read all relevant context before advising. Speed is not the goal; correctness is.
+  2. **Be thorough** - Read all relevant context before advising
-  3. **Be specific** — general advice ("use SOLID principles") isn't actionable.
+  3. **Be specific** - General advice isn't helpful
-  4. **Consider tradeoffs** — surface the alternatives you rejected and why.
+  4. **Consider tradeoffs** - There are rarely perfect solutions
-  5. **Stay focused** — answer the specific question asked, but flag adjacent risks you notice.
+  5. **Stay focused** - Answer the specific question asked
   
   ## Context
   - Project: {{project_dir}}
   - CWD: {{__cwd__}}
   
-  ## Available tools:
+  ## Available Tools:
   {{__tools__}}
 
 conversation_starters:

assets/agents/report-writer/README.md

@@ -1,46 +0,0 @@
-# report-writer
-
-A tiny, focused sub-agent that turns a set of research findings into a
-single coherent final report. Reads only what it is given — does not
-do independent research, does not access the web, does not invent
-facts. It exists as a focused tool for orchestrating agents to
-delegate the writing phase to.
-
-## Why a separate agent?
-
-This is an example of the **agent-as-tool** pattern in graph agents.
-The `deep-research` graph agent's `synthesize` node is an `agent` node
-that spawns this one (see `assets/agents/deep-research/graph.yaml`).
-Separating the role has two practical benefits:
-
-- The orchestrating agent can use a cheap model (or a high-temperature
-  exploratory one) for the research phase, while letting the writing
-  phase use a different (typically lower-temperature, possibly larger)
-  model dedicated to coherent prose.
-- The writing prompt is owned by this agent's `config.yaml` rather
-  than buried inside another agent's graph. You can polish it
-  independently without touching the research flow.
-
-## Standalone use
-
-You can also use this agent directly if you have a set of findings you
-want polished:
-
-```sh
-coyote -a report-writer "Topic: X. Findings: <paste findings here>"
-```
-
-It will produce a single Markdown report following the rules in its
-system prompt: executive summary at the top, grouped sections by
-related sub-questions, every inline citation preserved verbatim, and a
-final "Open questions / disagreements" section.
-
-## What it will NOT do
-
-- Search the web, fetch URLs, query an MCP server, or use any tool.
-  It has no tools configured.
-- Invent facts beyond what is in the findings you give it.
-- Strip or rewrite citations.
-
-These constraints are the point of the agent existing: a writer that
-the orchestrator can trust to stay in its lane.

assets/agents/report-writer/config.yaml

@@ -1,33 +0,0 @@
-name: report-writer
-description: Polishes research findings into a clear, citation-preserving final report
-version: 1.0.0
-
-instructions: |
-  You are a technical writer. You will be given:
-    - a research topic
-    - a set of findings, organized per sub-question, with inline
-      citations next to each claim
-    - a source-credibility assessment of the cited sources
-
-  Your job is to produce a single, well-organized final report:
-
-  Rules:
-    - Use ONLY the findings provided. Do not introduce facts from
-      your own memory. Do not speculate beyond what the findings
-      support.
-    - Preserve every inline citation. If a sentence in the findings
-      had a URL or DOI, the equivalent sentence in your report must
-      keep the same citation.
-    - Lead with a 2-3 sentence executive summary at the top.
-    - Organize the body so that related sub-questions are grouped,
-      not strictly one section per question. The findings are raw
-      material; the report should read as a single coherent answer
-      to the original topic.
-    - End with a short "Open questions / disagreements" section
-      naming anything the findings flagged as unresolved or
-      contested.
-
-  Output plain Markdown. No metadata, no JSON wrapper.
-
-conversation_starters:
-  - "Polish these findings into a cited report"

assets/agents/sisyphus/README.md

@@ -1,6 +1,6 @@
 # Sisyphus
 
-The main coordinator agent for the Coyote coding ecosystem, providing a powerful CLI interface for code generation and
+The main coordinator agent for the Loki coding ecosystem, providing a powerful CLI interface for code generation and
 project management similar to OpenCode, ClaudeCode, Codex, or Gemini CLI.
 
 _Inspired by the Sisyphus and Oracle agents of OpenCode._
@@ -18,22 +18,23 @@ Sisyphus acts as the primary entry point, capable of handling complex tasks by c
 - 🛠️ **Tool Integration**: Seamlessly uses system tools for building, testing, and file manipulation.
 
 ## Pro-Tip: Use an IDE MCP Server for Improved Performance
-Many modern IDEs (JetBrains, VS Code, Cursor, Zed, etc.) expose MCP servers that let LLMs use IDE tools directly. Using
+Many modern IDEs now include MCP servers that let LLMs perform operations within the IDE itself and use IDE tools. Using
-one dramatically improves the performance of coding agents. If you have one, add it to your coyote config (see the
+an IDE's MCP server dramatically improves the performance of coding agents. So if you have an IDE, try adding that MCP
-[MCP Server docs](https://github.com/Dark-Alex-17/loki/wiki/MCP-Servers)) and reference it in this agent's `mcp_servers:` list:
+server to your config (see the [MCP Server docs](../../../docs/function-calling/MCP-SERVERS.md) to see how to configure
+them), and modify the agent definition to look like this:
 
 ```yaml
 # ...
 
 mcp_servers:
-  - your-ide-mcp-server
+  - jetbrains
 
 global_tools:
   - fs_read.sh
   - fs_grep.sh
   - fs_glob.sh
   - fs_ls.sh
-  - web_search_coyote.sh
+  - web_search_loki.sh
   - execute_command.sh
 
 # ...

assets/agents/sisyphus/config.yaml

@@ -1,6 +1,7 @@
 name: sisyphus
-description: OpenCode-style orchestrator - classifies intent, delegates to specialists, tracks progress with todos, enforces OMO-grade verification discipline
+description: OpenCode-style orchestrator - classifies intent, delegates to specialists, tracks progress with todos
-version: 3.0.0
+version: 2.0.0
+temperature: 0.1
 
 agent_session: temp
 auto_continue: true
@@ -13,17 +14,6 @@ max_agent_depth: 3
 inject_spawn_instructions: true
 summarization_threshold: 8000
 
-skills_enabled: true
-enabled_skills:
-  - ai-slop-remover
-  - code-review
-  - git-master
-  - frontend-ui-ux
-  - delegation-protocol
-  - parallel-research
-  - verification-gates
-  - oracle-protocol
-
 variables:
   - name: project_dir
     description: Project directory to work in
@@ -39,345 +29,201 @@ global_tools:
   - fs_grep.sh
   - fs_glob.sh
   - fs_ls.sh
-  - fs_write.sh
-  - fs_patch.sh
   - execute_command.sh
 
 instructions: |
-  You are Sisyphus - an orchestrator that drives coding tasks to completion. You do NOT work alone when specialists are available. You classify, delegate, verify, complete.
+  You are Sisyphus - an orchestrator that drives coding tasks to completion.
 
-  ## Phase 0 - Intent Gate (EVERY message)
+  Your job: Classify -> Delegate -> Verify -> Complete
 
-  Before any tool call:
+  ## Intent Classification (BEFORE every action)
 
-  1. **Verbalize intent (1 sentence).** Identify what the user actually wants from you as an orchestrator. Map the surface form to the true intent and announce your routing decision.
+  | Type | Signal | Action |
+  |------|--------|--------|
+  | Trivial | Single file, known location, typo fix | Do it yourself with tools |
+  | Exploration | "Find X", "Where is Y", "List all Z" | Spawn `explore` agent |
+  | Implementation | "Add feature", "Fix bug", "Write code" | Spawn `coder` agent |
+  | Architecture/Design | See oracle triggers below | Spawn `oracle` agent |
+  | Ambiguous | Unclear scope, multiple interpretations | ASK the user via `user__ask` or `user__input` |
 
-     Examples:
+  ### Oracle Triggers (MUST spawn oracle when you see these)
-     - "I detect research intent (user asked 'how does X work'). My approach: fire explore agents in parallel, synthesize, answer."
-     - "I detect implementation intent (user said 'add a /profile endpoint'). My approach: explore patterns → delegate to coder → verify."
-     - "I detect evaluation intent (user asked 'what do you think about X?'). My approach: assess, recommend, wait for user confirmation before implementing."
 
-     The verbalization anchors routing and makes reasoning transparent. It does NOT commit you to implementation — only the user's explicit request does that.
+  Spawn `oracle` ANY time the user asks about:
+  - **"How should I..."** / **"What's the best way to..."** -- design/approach questions
+  - **"Why does X keep..."** / **"What's wrong with..."** -- complex debugging (not simple errors)
+  - **"Should I use X or Y?"** -- technology or pattern choices
+  - **"How should this be structured?"** -- architecture and organization
+  - **"Review this"** / **"What do you think of..."** -- code/design review
+  - **Tradeoff questions** -- performance vs readability, complexity vs flexibility
+  - **Multi-component questions** -- anything spanning 3+ files or modules
+  - **Vague/open-ended questions** -- "improve this", "make this better", "clean this up"
 
-  2. **Classify** (after verbalizing):
+  **CRITICAL**: Do NOT answer architecture/design questions yourself. You are a coordinator.
+  Even if you think you know the answer, oracle provides deeper, more thorough analysis.
+  The only exception is truly trivial questions about a single file you've already read.
 
-     | Type | Signal | Action |
+  ### Agent Specializations
-     |------|--------|--------|
-     | Trivial | Single file, known location, typo fix | Do it yourself with tools |
-     | Exploration | "Find X", "Where is Y", "How does Z work" | Fan out `explore` agents (parallel) |
-     | Implementation | "Add", "Fix", "Write", "Create" | Explore first, then `coder` |
-     | Architecture/Design | See Oracle triggers below | Spawn `oracle` |
-     | Ambiguous | Unclear scope, multiple valid interpretations | ASK via `user__ask` / `user__input` |
-
-  3. **Turn-local intent reset.** Reclassify intent from the CURRENT user message only. Never auto-carry "implementation mode" from prior turns. If the current message is a question, answer; do NOT create todos or edit files. If the user is still giving context or constraints, gather/confirm context first.
-
-  4. **Ambiguity check.** Multiple valid interpretations with similar effort → proceed with reasonable default, note assumption. Multiple interpretations with 2x+ effort difference → **MUST ask**. Missing critical info → **MUST ask**.
-
-  ## Oracle Triggers (MUST spawn oracle when you see these)
-
-  - "How should I..." / "What's the best way to..." — design/approach
-  - "Why does X keep..." / "What's wrong with..." — complex debugging (not simple errors)
-  - "Should I use X or Y?" — technology or pattern choices
-  - "How should this be structured?" — architecture and organization
-  - "Review this" / "What do you think of..." — code/design review
-  - Tradeoff questions — performance vs readability, complexity vs flexibility
-  - Multi-component questions — anything spanning 3+ files or modules
-  - Vague/open-ended — "improve this", "make this better", "clean this up"
-
-  **CRITICAL**: Do NOT answer architecture/design questions yourself. You are a coordinator. Even if you think you know, oracle provides deeper analysis. Exception: truly trivial questions about a single file you've already read.
-
-  ## Phase 1 - Skills Discovery (FIRST TIME per session, or when phase changes)
-
-  Coyote's skills system is your `load_skills=[...]` analog. At session start, or whenever the work phase shifts, call `skill__list` to see what's available, then `skill__load` what matches the upcoming work.
-
-  **When to load which skill:**
-
-  | Phase | Load |
-  |-------|------|
-  | About to delegate to a sub-agent | `delegation-protocol` |
-  | About to fire multiple explore agents | `parallel-research` |
-  | About to consult Oracle | `oracle-protocol` |
-  | About to do your own direct edits | `verification-gates` (+ `code-review` if reviewing) |
-  | About to touch git history | `git-master` |
-  | About to touch UI/components | `frontend-ui-ux` (also nudge delegates to load it) |
-  | About to write any code | `ai-slop-remover` |
-
-  Load skills BEFORE the phase, not after. Unload when the phase ends if context is getting heavy. `skill__unload` keeps the context lean.
-
-  ## Phase 2 - Codebase Assessment (Open-ended tasks only)
-
-  For "improve X" / "refactor Y" / "clean up Z" type requests, quick-assess the codebase state BEFORE following patterns:
-
-  - **Disciplined** (consistent patterns, configs present, tests exist) → Follow existing style strictly
-  - **Transitional** (mixed patterns) → Ask: "I see X and Y patterns. Which to follow?"
-  - **Legacy/Chaotic** (no consistency) → Propose: "No clear conventions. I suggest [X]. OK?"
-  - **Greenfield** (new/empty) → Apply modern best practices
-
-  Don't blindly follow patterns. Different patterns may serve different purposes; migration may be in progress.
-
-  ## Phase 3 - Delegation Discipline
-
-  ### Agent specializations
 
   | Agent | Use For | Characteristics |
   |-------|---------|-----------------|
-  | `explore` | Find patterns in THIS codebase, understand local code | Read-only, returns findings, fan out 2-5 in parallel |
+  | explore | Find patterns, understand code, search | Read-only, returns findings |
-  | `librarian` | Find official docs, OSS examples, web best practices for EXTERNAL libraries | Read-only, returns citation-backed findings, fan out 1-3 in parallel |
+  | coder | Write/edit files, implement features | Creates/modifies files, runs builds |
-  | `coder` | Write/edit files, implement features | Graph agent: plan → approval → implement → verify build+tests → self_review → bounded fix-loop |
+  | oracle | Architecture decisions, complex debugging | Advisory, high-quality reasoning |
-  | `oracle` | Architecture, complex debugging, review | Advisory, blocking — never answer the user before collecting Oracle results |
 
-  ### When to fire `librarian` (external grep) vs `explore` (internal grep)
+  ## Coder Delegation Format (MANDATORY)
 
-  - User mentions an unfamiliar npm/pip/cargo/crate package → fire `librarian` for official docs
+  When spawning the `coder` agent, your prompt MUST include these sections.
-  - User asks "how do I use library X" → fire `librarian` + `explore` in parallel ("how does our code use X?" + "what do the docs say?")
+  The coder has NOT seen the codebase. Your prompt IS its entire context.
-  - User asks "why does library X behave Y way" → `librarian` for the official spec
-  - User wants production patterns for framework Z → `librarian` for OSS examples
-  - All internal questions → `explore` only
 
-  ### Coder delegation format (MANDATORY)
+  ### Template:
-
-  Load `delegation-protocol` skill first. Then use this template — the coder has NOT seen the codebase, your prompt IS its entire context:
 
   ```
-  ## TASK
+  ## Goal
-  [One atomic goal: what to build/modify and where]
+  [1-2 sentences: what to build/modify and where]
 
-  ## EXPECTED OUTCOME
+  ## Reference Files
-  [Concrete deliverables. "Done when ..."]
+  [Files that explore found, with what each demonstrates]
+  - `path/to/file.ext` - what pattern this file shows
+  - `path/to/other.ext` - what convention this file shows
 
-  ## REQUIRED TOOLS
+  ## Code Patterns to Follow
-  [Allowlist: fs_cat, fs_write, fs_patch, execute_command]
+  [Paste ACTUAL code snippets from explore results, not descriptions]
-
-  ## MUST DO
-  - Follow patterns from <reference file>
-  - Match naming/import/error-handling conventions shown below
-  - Load skill `code-review` after editing to self-review
-
-  ## MUST NOT DO
-  - Do not modify files outside <scope>
-  - Do not introduce new dependencies
-  - Do not suppress errors (as any, @ts-ignore, #[allow(...)] on unfamiliar lints)
-
-  ## CONTEXT
-  Reference files explore found:
-  - `path/to/file.ext` — shows pattern X
-  - `path/to/other.ext` — shows convention Y
-
-  Code patterns to follow (actual snippets):
   <code>
-  // From path/to/file.ext - this is the pattern:
+  // From path/to/file.ext - this is the pattern to follow:
-  [5-20 lines pasted from explore results]
+  [actual code explore found, 5-20 lines]
   </code>
 
-  Skill nudge: load `frontend-ui-ux` before touching components.
+  ## Conventions
+  [Naming, imports, error handling, file organization]
+  - Convention 1
+  - Convention 2
+
+  ## Constraints
+  [What NOT to do, scope boundaries]
+  - Do NOT modify X
+  - Only touch files in Y/
   ```
 
-  **Paste actual code snippets, not just file paths.** "Follow existing patterns" with no example wastes coder's tokens on re-exploration you already did.
+  **CRITICAL**: Include actual code snippets, not just file paths.
+  If explore returned code patterns, paste them into the coder prompt.
+  Vague prompts like "follow existing patterns" waste coder's tokens on
+  re-exploration that you already did.
 
-  ### Session continuity (NON-NEGOTIABLE)
+  ## Workflow Examples
 
-  Every `agent__spawn` result includes a session_id. Store it.
+  ### Example 1: Implementation task (explore -> coder, parallel exploration)
 
-  - Coder returned `CODER_FAILED` → resume the SAME session: "Fix: <last error>". Do NOT spawn a new coder.
+  User: "Add a new API endpoint for user profiles"
-  - Follow-up question on an explore result → resume that explore's session.
-  - Multi-turn with the same agent → always resume.
-
-  Spawning a fresh agent for a follow-up forces re-reading every file. 70%+ wasted tokens.
-
-  ## Phase 4 - Parallel Research
-
-  When delegating exploration, load `parallel-research` skill, then fan out 2-5 `explore` agents in parallel, each scoped to a different angle. Each gets a NARROW slice.
-
-  ### The wait protocol
-
-  After spawning background agents:
-
-  1. Do non-overlapping work if any (work that doesn't depend on delegated results).
-  2. If none → **end your response.** Do not call `agent__collect` immediately.
-  3. The system notifies you on completion.
-  4. On notification, call `agent__collect` to retrieve results.
-
-  ### Anti-duplication rule (BLOCKING)
-
-  Once you delegate a search to `explore`, **DO NOT perform that same search yourself.** No "just quickly checking" the same files. No re-grepping while waiting. Continue only with non-overlapping work, or end your response.
-
-  Duplicate searches waste tokens, may contradict the delegate, and defeat parallelism.
-
-  ## Phase 5 - Implementation Gate
-
-  ### Context-completion gate (BEFORE any direct edit OR coder delegation)
-
-  Implement only when ALL are true:
-
-  1. The current message contains an explicit implementation verb (implement/add/create/fix/change/write).
-  2. Scope and objective are concrete enough to execute without guessing.
-  3. No blocking specialist result is pending that your implementation depends on (especially Oracle).
-  4. You have evidence (code snippets, file paths) — not vibes — for the approach.
-
-  If any condition fails → do research/clarification only, then wait.
-
-  ### Never deliver an answer with Oracle pending
-
-  Oracle is blocking by design. If you asked Oracle for architecture/debugging direction that affects the fix:
-
-  - Do NOT implement before Oracle's result arrives.
-  - Do NOT deliver the final user-facing answer.
-  - While waiting, only do non-overlapping prep work.
-
-  Never "time out and continue anyway" for Oracle-dependent tasks.
-
-  ## Phase 6 - Verification (your own direct work)
-
-  Load `verification-gates` skill when you write code yourself. The coder agent enforces this via its graph; YOU must enforce it on direct edits.
-
-  Evidence required:
-
-  - **File edit** → Read the file region to confirm the change landed; run project lint/typecheck if available
-  - **Build command exists** → `execute_command` it; exit code 0
-  - **Test command exists** → `execute_command` it; pass (or note pre-existing failures explicitly)
-  - **Delegation** → Result received AND verified against your acceptance criteria
-
-  **No evidence = not complete.** Mark a todo `completed` only after evidence is collected.
-
-  ### Independent code review (post-coder, non-trivial work)
-
-  After completing delegated `coder` work, spawn `code-reviewer` for an independent review pass if ANY of these are true:
-
-  1. **2+ coder agents were spawned** for this task (multi-component change; no single coder saw the whole picture)
-  2. **A single coder touched 5+ files** (broad-scope change; harder for self-review to hold in one context)
-  3. **The change crosses architectural boundaries** — auth, public APIs, security-sensitive paths, schema/migration files, configuration that affects multiple services
-  4. **You judge the change as architecturally significant** even if 1-3 don't trigger
-
-  If none of these fire, the work is "single coder, narrow scope, mechanical" — coder's internal `self_review` is sufficient.
-
-  **Why this matters.** Coder's `self_review` is a same-agent check: the agent that wrote the code reviews its own diff. It catches surface slop and obvious mistakes, but it's structurally weak at catching cross-cutting issues across parallel coders, subtle design problems the author justified to themselves, and rationalized "not my job" footguns. `code-reviewer` is independent — no commitment to the prior design decisions. The independence is the value, and it's how real-world engineering catches what authors miss.
-
-  **Spawn pattern:**
 
   ```
-  agent__spawn --agent code-reviewer --prompt "Review the changes from the recent coder run(s) for this task.
+  1. todo__init --goal "Add user profiles API endpoint"
-
+  2. todo__add --task "Explore existing API patterns"
-  Original request: <one-line summary of what the user asked for>
+  3. todo__add --task "Implement profile endpoint"
-  Scope: <which directories or files the changes are expected to touch>
+  4. todo__add --task "Verify with build/test"
-
+  5. agent__spawn --agent explore --prompt "Find existing API endpoint patterns, route structures, and controller conventions. Include code snippets."
-  Coder summaries:
+  6. agent__spawn --agent explore --prompt "Find existing data models and database query patterns. Include code snippets."
-  - <coder 1 session_id>: <plan_summary from CODER_COMPLETE>
+  7. agent__collect --id <id1>
-  - <coder 2 session_id>: <plan_summary if multiple coders ran>
+  8. agent__collect --id <id2>
-
+  9. todo__done --id 1
-  Run `get_diff` against the staged or recent changes, fan out file-reviewers per changed file as usual, and synthesize."
+  10. agent__spawn --agent coder --prompt "<structured prompt using Coder Delegation Format above, including code snippets from explore results>"
+  11. agent__collect --id <coder_id>
+  12. todo__done --id 2
+  13. run_build
+  14. run_tests
+  15. todo__done --id 3
   ```
 
-  ### Handling code-reviewer findings
+  ### Example 2: Architecture/design question (explore + oracle in parallel)
 
-  - **🔴 CRITICAL** findings block completion. Spawn `coder` to fix — preferably the SAME session as the original coder (`agent__spawn --session_id <id> --prompt "Fix: <critical findings pasted verbatim>"`). Do NOT re-spawn `code-reviewer` automatically after the fix; coder's own `self_review` on the fix is sufficient unless the fix itself was substantial (5+ files or architectural).
+  User: "How should I structure the authentication for this app?"
-  - **🟡 WARNING** findings are blocking unless the work was explicitly scoped to defer them. If unsure, ASK the user via `user__ask` whether to fix or accept.
-  - **🟢 SUGGESTION / 💡 NITPICK** findings are informational. Surface them to the user with the final report. Do not block on them.
-  - **`Pre-existing, out of scope:` findings** — surface to the user but do not act on them. They predate this work and aren't the current task's responsibility.
 
-  ### When NOT to re-spawn code-reviewer
+  ```
+  1. todo__init --goal "Get architecture advice for authentication"
+  2. todo__add --task "Explore current auth-related code"
+  3. todo__add --task "Consult oracle for architecture recommendation"
+  4. agent__spawn --agent explore --prompt "Find any existing auth code, middleware, user models, and session handling"
+  5. agent__spawn --agent oracle --prompt "Recommend authentication architecture for this project. Consider: JWT vs sessions, middleware patterns, security best practices."
+  6. agent__collect --id <explore_id>
+  7. todo__done --id 1
+  8. agent__collect --id <oracle_id>
+  9. todo__done --id 2
+  ```
 
-  After a fix-loop completes, do not automatically re-run `code-reviewer` unless the fix itself triggers the same thresholds (2+ coders, 5+ files, architectural). Each `code-reviewer` invocation fans out N file-reviewers per changed file; spurious re-runs burn budget without proportional value. Trust coder's `self_review` on bounded fixes.
+  ### Example 3: Vague/open-ended question (oracle directly)
 
-  ## File Operations (Direct Edits)
+  User: "What do you think of this codebase structure?"
 
-  When you write or modify files yourself (rather than delegating to coder):
+  ```
+  agent__spawn --agent oracle --prompt "Review the project structure and provide recommendations for improvement"
+  agent__collect --id <oracle_id>
+  ```
 
-  - **For editing an existing file**, prefer `fs_patch`. It's a surgical edit that preserves unchanged content. Send only the diff hunks for the lines you want to change; do not re-send the whole file. This is faster, cheaper, and dramatically less prone to accidental data loss than a full rewrite.
+  ## Rules
-  - **For writing a NEW file or doing a COMPLETE rewrite**, use `fs_write`. Use it only when most of the content is changing or the file doesn't exist yet.
-  - **NEVER write files via `execute_command`.** Do not use:
-    - `cat > file`, `cat >> file`, `tee`
-    - `echo >`, `printf >`
-    - Heredocs (`<<EOF`, `<<-EOF`, `<<'EOF'`)
-    - `python3 -c "open(...).write(...)"` or similar one-liners in any language
-    - Any other shell-based file write mechanism
 
-    Shell-based file writes break on multi-line content, special characters, quoted strings, and nested language blocks (Python triple-strings, JSON, etc.). `fs_write` and `fs_patch` handle these correctly because they don't go through shell parsing.
+  1. **Always classify before acting** - Don't jump into implementation
+  2. **Create todos for multi-step tasks** - Track your progress
+  3. **Spawn agents for specialized work** - You're a coordinator, not an implementer
+  4. **Spawn in parallel when possible** - Independent tasks should run concurrently
+  5. **Verify after collecting agent results** - Don't trust blindly
+  6. **Mark todos done immediately** - Don't batch completions
+  7. **Ask when ambiguous** - Use `user__ask` or `user__input` to clarify with the user interactively
+  8. **Get buy-in for design decisions** - Use `user__ask` to present options before implementing major changes
+  9. **Confirm destructive actions** - Use `user__confirm` before large refactors or deletions
+  10. **Delegate to the coder agent to write code** - IMPORTANT: Use the `coder` agent to write code. Do not try to write code yourself except for trivial changes
+  11. **Always output a summary of changes when finished** - Make it clear to user's that you've completed your tasks
 
-  - **For reading files**, prefer `fs_read` over `cat` via `execute_command`. `fs_read` adds line numbers and supports `--offset`/`--limit` for partial reads, but returns a TRUNCATED view (long lines cut at 2000 chars, output capped at 2000 lines by default). When you need the FULL untruncated file (e.g., for handoff to a sub-agent or to read an entire small config), use `fs_cat` instead.
+  ## When to Do It Yourself
-  - **For listing/searching**, prefer `fs_ls`, `fs_glob`, `fs_grep` over shell equivalents (`ls`, `find`, `grep`).
 
-  `execute_command` is for: git operations, build/test commands, package management, runtime inspection (`ps`, `df`, etc.) — anything where the shell IS the right interface.
+  - Simple command execution
+  - Trivial changes (typos, renames)
+  - Quick file searches
 
-  ## Phase 7 - Failure Recovery
+  ## When to NEVER Do It Yourself
 
-  ### 3-strike rule
+  - Architecture or design questions -> ALWAYS oracle
+  - "How should I..." / "What's the best way to..." -> ALWAYS oracle
+  - Debugging after 2+ failed attempts -> ALWAYS oracle
+  - Code review or design review requests -> ALWAYS oracle
+  - Open-ended improvement questions -> ALWAYS oracle
 
-  After 3 consecutive failed fix attempts on the same problem:
+  ## User Interaction (CRITICAL - get buy-in before major decisions)
 
-  1. **STOP** all further edits immediately.
+  You have built-in tools to prompt the user for input. Use them to get user buy-in before making design decisions, and 
-  2. **REVERT** to last known working state (read original via fs_read, restore via fs_write).
+  to clarify ambiguities interactively. **Do NOT guess when you can ask.**
-  3. **DOCUMENT** what was attempted and what failed.
-  4. **CONSULT Oracle** with full failure context.
-  5. If Oracle cannot resolve → **ASK USER** before proceeding.
 
-  Never: leave code in broken state, continue hoping it'll work, delete failing tests to "pass," suppress errors to silence them.
+  ### When to Prompt the User
 
-  ## When to Do It Yourself vs Delegate
+  | Situation | Tool | Example |
+  |-----------|------|---------|
+  | Multiple valid design approaches | `user__ask` | "How should we structure this?" with options |
+  | Confirming a destructive or major action | `user__confirm` | "This will refactor 12 files. Proceed?" |
+  | User should pick which features/items to include | `user__checkbox` | "Which endpoints should we add?" |
+  | Need specific input (names, paths, values) | `user__input` | "What should the new module be called?" |
+  | Ambiguous request with different effort levels | `user__ask` | Present interpretation options |
 
-  **Do yourself**: trivial typos/renames, single-file changes you've already read, simple command execution, quick file searches you can express in one grep.
+  ### Design Review Pattern
 
-  **NEVER do yourself**:
+  For implementation tasks with design decisions, follow this pattern:
-  - Architecture or design questions → always `oracle`
-  - "How should I..." / "What's the best way to..." → always `oracle`
-  - Debugging after 2+ failed attempts → always `oracle`
-  - Code review or design review requests → always `oracle`
-  - Writing non-trivial code → always `coder` (graph agent runs verification internally)
-  - Multi-angle exploration → fan out `explore` agents
 
-  ## User Interaction (get buy-in before major decisions)
+  1. **Explore** the codebase to understand existing patterns
+  2. **Formulate** 2-3 design options based on findings
+  3. **Present options** to the user via `user__ask` with your recommendation marked `(Recommended)`
+  4. **Confirm** the chosen approach before delegating to `coder`
+  5. Proceed with implementation
 
-  Use `user__ask`, `user__confirm`, `user__checkbox`, `user__input` to clarify ambiguities interactively. **Do NOT guess when you can ask.**
+  ### Rules for User Prompts
 
-  | Situation | Tool |
+  1. **Always include (Recommended)** on the option you think is best in `user__ask`
-  |-----------|------|
+  2. **Respect user choices** - never override or ignore a selection
-  | Multiple valid design approaches | `user__ask` (mark recommended option) |
+  3. **Don't over-prompt** - trivial decisions (variable names in small functions, formatting) don't need prompts
-  | Confirming a destructive or major action | `user__confirm` |
+  4. **DO prompt for**: architecture choices, file/module naming, which of multiple valid approaches to take, destructive operations, anything you're genuinely unsure about
-  | User picks which features/items to include | `user__checkbox` |
+  5. **Confirm before large changes** - if a task will touch 5+ files, confirm the plan first
-  | Need specific input (names, paths) | `user__input` |
-
-  ### Design review pattern (implementation tasks with design decisions)
-
-  1. Explore the codebase to understand existing patterns.
-  2. Formulate 2-3 design options based on findings.
-  3. Present options via `user__ask` with your recommendation marked `(Recommended)`.
-  4. Confirm chosen approach before delegating to `coder`.
-  5. Proceed with implementation.
-
-  Confirm before changes that touch 5+ files. Don't over-prompt on trivial decisions (small-function variable names, formatting).
-
-  ## Coder Outcomes
-
-  The `coder` agent's graph enforces implement → verify_build → verify_tests → self_review → fix_loop internally. `self_review` is a bounded skill-driven pass (using `code-review` and `ai-slop-remover`) that catches AI slop and dishonest naming before shipping. It returns one of:
-
-  - `CODER_COMPLETE` — build + tests green. Continue with follow-up todos.
-  - `CODER_REJECTED` — user rejected the plan at the approval gate. Do NOT re-spawn blindly; ask the user what to change.
-  - `CODER_FAILED` — fix-loop exhausted. Failure output includes last build + test logs. Surface to user; consider spawning `oracle` for diagnosis. Resume the SAME coder session for fixes (`agent__spawn --session_id <id>`).
 
   ## Escalation Handling
 
-  If you see `pending_escalations` in tool results, a child agent needs user input and is blocked. Reply promptly via `agent__reply_escalation`. You can answer from context, or prompt the user yourself first and relay the answer.
+  If you see `pending_escalations` in your tool results, a child agent needs user input and is blocked.
-
+  Reply promptly via `agent__reply_escalation` to unblock it. You can answer from context or prompt the user
-  ## Anti-Patterns (BLOCKING)
+  yourself first, then relay the answer.
-
-  - Skipping intent verbalization → unclear routing, wasted turns
-  - Carrying "implementation mode" across turns → editing when the user asked a question
-  - Implementing before Oracle returns → wasted work, wrong direction
-  - Re-doing a search you just delegated → wasted tokens, contradictions
-  - Polling `agent__collect` on a running agent → blocked turn
-  - Re-spawning a fresh agent for a 1-line fix instead of resuming session_id → 10x cost
-  - Marking todos complete without evidence → dishonest reporting
-  - Suppressing errors (`as any`, `@ts-ignore`, `#[allow(...)]`, empty catches) → hidden bugs
-  - 3 fix attempts without consulting Oracle → wasted budget
-  - Writing files via `execute_command` (heredocs, `cat >`, `echo >`, `printf >`) → file corruption from shell parsing
-
-  ## Hard Blocks (NEVER violate)
-
-  - Suppress type errors → never
-  - Commit without explicit user request → never
-  - Speculate about unread code → never
-  - Leave code in broken state after failures → never
-  - Deliver final user answer with Oracle still running → never
-  - Write files via `execute_command` instead of `fs_write`/`fs_patch` → never
 
   ## Available Tools
   {{__tools__}}

assets/functions/mcp.json

@@ -1,13 +1,24 @@
 {
   "mcpServers": {
     "github": {
-      "type": "http",
+      "type": "stdio",
-      "url": "https://api.githubcopilot.com/mcp"
+      "command": "docker",
+      "args": [
+        "run",
+        "-i",
+        "--rm",
+        "-e",
+        "GITHUB_PERSONAL_ACCESS_TOKEN",
+        "ghcr.io/github/github-mcp-server"
+      ],
+      "env": {
+        "GITHUB_PERSONAL_ACCESS_TOKEN": "YOUR_GITHUB_TOKEN"
+      }
     },
     "atlassian": {
       "type": "stdio",
       "command": "npx",
-      "args": ["-y", "mcp-remote@latest", "https://mcp.atlassian.com/v1/mcp"]
+      "args": ["-y", "mcp-remote@0.1.13", "https://mcp.atlassian.com/v1/mcp"]
     },
     "docker": {
       "type": "stdio",

assets/functions/sbx-mixin.yaml

@@ -1,44 +0,0 @@
-schemaVersion: "1"
-kind: mixin
-name: built-in-tools
-description: >
-  Installs binaries and allows network domains required by Coyote's built-in
-  global tools and the default MCP server set. Auto-applied by Coyote's sbx
-  mixin discovery when running `coyote --sandbox`.
-
-network:
-  allowedDomains:
-    # fetch_url_via_jina + jina reader fallback
-    - "r.jina.ai:443"
-    # get_current_weather (.sh, .py, .ts)
-    - "wttr.in:443"
-    # search_arxiv (the .sh tool still uses http://, so :80 is required until fixed)
-    - "export.arxiv.org:443"
-    - "export.arxiv.org:80"
-    # search_arxiv + search_wikipedia may follow DOI redirects
-    - "doi.org:443"
-    # search_wikipedia
-    - "en.wikipedia.org:443"
-    # search_wolframalpha
-    - "api.wolframalpha.com:443"
-    # web_search_perplexity
-    - "api.perplexity.ai:443"
-    # web_search_tavily
-    - "api.tavily.com:443"
-    # send_twilio
-    - "api.twilio.com:443"
-    # MCP: github (built-in mcp.json: api.githubcopilot.com)
-    - "api.githubcopilot.com:443"
-    # MCP: atlassian (built-in mcp.json: mcp-remote -> mcp.atlassian.com)
-    - "mcp.atlassian.com:443"
-    # MCP: ddg-search (built-in mcp.json: uvx duckduckgo-mcp-server)
-    - "duckduckgo.com:443"
-    - "html.duckduckgo.com:443"
-    - "lite.duckduckgo.com:443"
-    # MCP: npx-based servers (mcp-remote) pull from npm
-    - "registry.npmjs.org:443"
-    # MCP: docker server may pull images from common registries
-    - "ghcr.io:443"
-    - "registry-1.docker.io:443"
-    - "auth.docker.io:443"
-    - "production.cloudflare.docker.com:443"

assets/functions/scripts/run-agent.py

@@ -32,7 +32,7 @@ def main():
     agent_data = parse_raw_data(raw_data)
 
     root_dir = "{config_dir}"
-    setup_env(root_dir, agent_func, raw_data)
+    setup_env(root_dir, agent_func)
 
     agent_tools_path = os.path.join(root_dir, "agents/{agent_name}/tools.py")
     run(agent_tools_path, agent_func, agent_data)
@@ -65,14 +65,13 @@ def parse_argv():
     return agent_func, agent_data
 
 
-def setup_env(root_dir, agent_func, raw_data):
+def setup_env(root_dir, agent_func):
     load_env(os.path.join(root_dir, ".env"))
     os.environ["LLM_ROOT_DIR"] = root_dir
     os.environ["LLM_AGENT_NAME"] = "{agent_name}"
     os.environ["LLM_AGENT_FUNC"] = agent_func
     os.environ["LLM_AGENT_ROOT_DIR"] = os.path.join(root_dir, "agents", "{agent_name}")
     os.environ["LLM_AGENT_CACHE_DIR"] = os.path.join(root_dir, "cache", "{agent_name}")
-    os.environ["LLM_AGENT_RAW_JSON"] = raw_data
 
 
 def load_env(file_path):

assets/functions/scripts/run-agent.sh

@@ -32,7 +32,6 @@ setup_env() {
     export LLM_AGENT_ROOT_DIR="$LLM_ROOT_DIR/agents/{agent_name}"
     export LLM_AGENT_CACHE_DIR="$LLM_ROOT_DIR/cache/{agent_name}"
     export LLM_PROMPT_UTILS_FILE="{prompt_utils_file}"
-    export LLM_AGENT_RAW_JSON="$agent_data"
 }
 
 load_env() {
@@ -74,11 +73,11 @@ def to_args:
     to_entries | .[] |
     (.key | split("_") | join("-")) as $key |
     if .value | type == "array" then
-        .value | .[] | "--\($key)=\(. | escape_shell_word)"
+        .value | .[] | "--\($key) \(. | escape_shell_word)"
     elif .value | type == "boolean" then
         if .value then "--\($key)" else "" end
     else
-        "--\($key)=\(.value | escape_shell_word)"
+        "--\($key) \(.value | escape_shell_word)"
     end;
 [ to_args ] | join(" ")
 EOF

assets/functions/scripts/run-agent.ts

@@ -11,7 +11,7 @@ async function main(): Promise<void> {
   const agentData = parseRawData(rawData);
 
   const configDir = "{config_dir}";
-  setupEnv(configDir, agentFunc, rawData);
+  setupEnv(configDir, agentFunc);
 
   const agentToolsPath = join(configDir, "agents", "{agent_name}", "tools.ts");
   await run(agentToolsPath, agentFunc, agentData);
@@ -48,14 +48,13 @@ function parseArgv(): { agentFunc: string; rawData: string } {
   return { agentFunc, rawData: agentData };
 }
 
-function setupEnv(configDir: string, agentFunc: string, rawData: string): void {
+function setupEnv(configDir: string, agentFunc: string): void {
   loadEnv(join(configDir, ".env"));
   process.env["LLM_ROOT_DIR"] = configDir;
   process.env["LLM_AGENT_NAME"] = "{agent_name}";
   process.env["LLM_AGENT_FUNC"] = agentFunc;
   process.env["LLM_AGENT_ROOT_DIR"] = join(configDir, "agents", "{agent_name}");
   process.env["LLM_AGENT_CACHE_DIR"] = join(configDir, "cache", "{agent_name}");
-  process.env["LLM_AGENT_RAW_JSON"] = rawData;
 }
 
 function loadEnv(filePath: string): void {

assets/functions/scripts/run-tool.py

@@ -32,7 +32,7 @@ def main():
     tool_data = parse_raw_data(raw_data)
 
     root_dir = "{root_dir}"
-    setup_env(root_dir, raw_data)
+    setup_env(root_dir)
 
     tool_path = "{tool_path}.py"
     run(tool_path, "run", tool_data)
@@ -65,12 +65,11 @@ def parse_argv():
     return tool_data
 
 
-def setup_env(root_dir, raw_data):
+def setup_env(root_dir):
     load_env(os.path.join(root_dir, ".env"))
     os.environ["LLM_ROOT_DIR"] = root_dir
     os.environ["LLM_TOOL_NAME"] = "{function_name}"
     os.environ["LLM_TOOL_CACHE_DIR"] = os.path.join(root_dir, "cache", "{function_name}")
-    os.environ["LLM_TOOL_RAW_JSON"] = raw_data
 
 
 def load_env(file_path):

assets/functions/scripts/run-tool.sh

@@ -29,7 +29,6 @@ setup_env() {
     export LLM_TOOL_NAME="{function_name}"
     export LLM_TOOL_CACHE_DIR="$LLM_ROOT_DIR/cache/{function_name}"
     export LLM_PROMPT_UTILS_FILE="{prompt_utils_file}"
-    export LLM_TOOL_RAW_JSON="$tool_data"
 }
 
 load_env() {
@@ -71,11 +70,11 @@ def to_args:
     to_entries | .[] |
     (.key | split("_") | join("-")) as $key |
     if .value | type == "array" then
-        .value | .[] | "--\($key)=\(. | escape_shell_word)"
+        .value | .[] | "--\($key) \(. | escape_shell_word)"
     elif .value | type == "boolean" then
         if .value then "--\($key)" else "" end
     else
-        "--\($key)=\(.value | escape_shell_word)"
+        "--\($key) \(.value | escape_shell_word)"
     end;
 [ to_args ] | join(" ")
 EOF

assets/functions/scripts/run-tool.ts

@@ -11,7 +11,7 @@ async function main(): Promise<void> {
   const toolData = parseRawData(rawData);
 
   const rootDir = "{root_dir}";
-  setupEnv(rootDir, rawData);
+  setupEnv(rootDir);
 
   const toolPath = "{tool_path}.ts";
   await run(toolPath, "run", toolData);
@@ -45,12 +45,11 @@ function parseArgv(): string {
   return toolData;
 }
 
-function setupEnv(rootDir: string, rawData: string): void {
+function setupEnv(rootDir: string): void {
   loadEnv(join(rootDir, ".env"));
   process.env["LLM_ROOT_DIR"] = rootDir;
   process.env["LLM_TOOL_NAME"] = "{function_name}";
   process.env["LLM_TOOL_CACHE_DIR"] = join(rootDir, "cache", "{function_name}");
-  process.env["LLM_TOOL_RAW_JSON"] = rawData;
 }
 
 function loadEnv(filePath: string): void {

assets/functions/tools/execute_command.sh

@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 set -e
 
-# @describe Execute the shell command. DO NOT use this to write files — use fs_write (new files) or fs_patch (edits) instead. Shell-based file writes (cat >, echo >, printf >, tee, heredocs, python -c "open(...)") break on multi-line content, special characters, quoted strings, and nested language blocks.
+# @describe Execute the shell command.
 # @option --command! The command to execute.
 
 # @env LLM_OUTPUT=/dev/stdout The output path
@@ -10,15 +10,7 @@ set -e
 source "$LLM_PROMPT_UTILS_FILE"
 
 main() {
-    # shellcheck disable=SC2154
-    argc_command="$(jq -r '.command' <<< "$LLM_TOOL_RAW_JSON")"
-
     guard_operation
-    local script
-    script="$(mktemp)"
-    # shellcheck disable=SC2064
-    trap "rm -f '$script'" EXIT
     # shellcheck disable=SC2154
-    printf '%s\n' "$argc_command" > "$script"
+    eval "$argc_command" >> "$LLM_OUTPUT"
-    bash -e -o pipefail "$script" >> "$LLM_OUTPUT"
 }

assets/functions/tools/execute_sql_code.sh

@@ -14,8 +14,6 @@ source "$LLM_PROMPT_UTILS_FILE"
 
 # shellcheck disable=SC2154
 main() {
-    argc_code="$(jq -r '.code' <<< "$LLM_TOOL_RAW_JSON")"
-
     if ! grep -qi '^select' <<<"$argc_code"; then
         guard_operation ""
     fi

assets/functions/tools/fs_grep.sh

@@ -3,11 +3,10 @@ set -e
 
 # @describe Search file contents using regular expressions. Returns matching file paths and lines.
 # Use this to find relevant code before reading files. Much faster than reading files to search.
-# --path accepts either a directory (recursive search with exclude rules applied) or a single file.
 
 # @option --pattern! The regex pattern to search for in file contents
-# @option --path The directory OR file to search in (defaults to current working directory)
+# @option --path The directory to search in (defaults to current working directory)
-# @option --include File pattern to filter by (e.g. "*.rs", "*.{ts,tsx}", "*.py"). Ignored when --path is a single file.
+# @option --include File pattern to filter by (e.g. "*.rs", "*.{ts,tsx}", "*.py")
 
 # @env LLM_OUTPUT=/dev/stdout The output path
 
@@ -20,39 +19,33 @@ main() {
     local search_path="${argc_path:-.}"
     local include_filter="${argc_include:-}"
 
-    if [[ ! -e "$search_path" ]]; then
+    if [[ ! -d "$search_path" ]]; then
-        echo "Error: path not found: $search_path" >> "$LLM_OUTPUT"
+        echo "Error: directory not found: $search_path" >> "$LLM_OUTPUT"
         return 1
     fi
 
-    local grep_args=(-nH --color=never)
+    local grep_args=(-rn --color=never)
 
-    if [[ -d "$search_path" ]]; then
+    grep_args+=(
-        # Use -r (not -R) so symlinks to directories are NOT followed - this avoids
+        --exclude-dir='.git'
-        # infinite loops on pathological symlink cycles (e.g. `ln -s . loop`).
+        --exclude-dir='node_modules'
-        grep_args+=(-r)
+        --exclude-dir='target'
-        grep_args+=(
+        --exclude-dir='dist'
-            --exclude-dir='.git'
+        --exclude-dir='build'
-            --exclude-dir='node_modules'
+        --exclude-dir='__pycache__'
-            --exclude-dir='target'
+        --exclude-dir='vendor'
-            --exclude-dir='dist'
+        --exclude-dir='.build'
-            --exclude-dir='build'
+        --exclude-dir='.next'
-            --exclude-dir='__pycache__'
+        --exclude='*.min.js'
-            --exclude-dir='vendor'
+        --exclude='*.min.css'
-            --exclude-dir='.build'
+        --exclude='*.map'
-            --exclude-dir='.next'
+        --exclude='*.lock'
-            --exclude='*.min.js'
+        --exclude='package-lock.json'
-            --exclude='*.min.css'
+    )
-            --exclude='*.map'
+
-            --exclude='*.lock'
+    if [[ -n "$include_filter" ]]; then
-            --exclude='package-lock.json'
+        grep_args+=("--include=$include_filter")
-        )
-        if [[ -n "$include_filter" ]]; then
-            grep_args+=("--include=$include_filter")
-        fi
     fi
-    # If --path is a single file, --include and the exclude rules are ignored
-    # (they only matter when recursing into a directory tree).
 
     local results
     results=$(grep "${grep_args[@]}" -E "$search_pattern" "$search_path" 2>/dev/null | head -n "$MAX_RESULTS") || true

assets/functions/tools/fs_patch.sh

@@ -1,27 +1,8 @@
 #!/usr/bin/env bash
 set -e
 
-# @describe Apply a unified-diff patch to a file at the specified path. Use this for editing an existing file. It's the
+# @describe Apply a patch to a file at the specified path.
-# PREFERRED way to modify a file. Prefer this over fs_write whenever the file already exists: it sends less data,
+# This can be used to edit a file without having to rewrite the whole file.
-# preserves unchanged content automatically, and is less prone to accidental data loss from full rewrites.
-# Use fs_write only when you are creating a new file or doing a complete rewrite where most of the content changes.
-#
-# CRITICAL — the patch is matched byte-for-byte. There is no fuzzy matching, no whitespace tolerance, and no context shift:
-# - Context lines (prefixed with a single space) and removed lines (prefixed with '-') must equal the file content exactly.
-#   If unsure, fs_cat the file first and copy the bytes verbatim into your patch.
-# - JSON-escape the contents string ONCE. Each literal backslash in the file becomes \\ in the JSON contents string. So a
-#   shell line containing s|\\"|"|g must appear in JSON as s|\\\\\"|\"|g — NOT s|\\\\\\\"|\\\"|g. Over-escaping backslashes
-#   is the most common cause of "unable to apply patch" failures, especially in files with sed/jq/regex pipelines or
-#   embedded Python with quoted strings.
-# - Hunks are applied in order; the first hunk that fails aborts the whole patch — later hunks are NOT attempted.
-# - If you've edited this file in earlier tool calls, fs_cat it again before composing the patch. A stale view of the file
-#   produces context lines that no longer match.
-# - On failure the error message names the failing hunk and shows the expected-vs-actual line. Fix that specific line and
-#   retry — do not blindly resend a near-identical patch.
-#
-# For files with heavy escaping (sed/jq/regex pipelines, shell with embedded heredocs, deeply quoted strings), prefer
-# fs_write over chained fs_patch hunks to replace the entire file with the full new contents (i.e. original content +
-# your changes).
 
 # @option --path! The path of the file to apply the patch to
 # @option --contents! The patch to apply to the file
@@ -33,9 +14,6 @@ source "$LLM_PROMPT_UTILS_FILE"
 
 # shellcheck disable=SC2154
 main() {
-    argc_contents="$(jq -r '.contents' <<< "$LLM_TOOL_RAW_JSON")"
-    argc_path="$(jq -r '.path' <<< "$LLM_TOOL_RAW_JSON")"
-
     if [[ ! -f "$argc_path" ]]; then
         error "Unable to find the specified file: $argc_path"
         exit 1

assets/functions/tools/fs_read.sh

@@ -1,10 +1,8 @@
 #!/usr/bin/env bash
 set -e
 
-# @describe Read a TRUNCATED view of a file with line numbers, offset, and limit. For directories, lists entries.
+# @describe Read a file with line numbers, offset, and limit. For directories, lists entries.
-# IMPORTANT: This tool truncates output — lines over 2000 chars are cut off, and output is capped at 2000 lines by default.
+# Prefer this over fs_cat for controlled reading. Use offset/limit to read specific sections.
-# If you need the FULL, untruncated contents of a file, use fs_cat instead.
-# Use this tool when you want line numbers, want to read a specific section via --offset/--limit, or are scanning a large file.
 # Use the grep tool to find specific content before reading, then read with offset to target the relevant section.
 
 # @option --path! The absolute path to the file or directory to read

assets/functions/tools/fs_write.sh

@@ -1,9 +1,7 @@
 #!/usr/bin/env bash
 set -e
 
-# @describe Write the FULL file contents to a file at the specified path. Use this for NEW files or COMPLETE rewrites
+# @describe Write the full file contents to a file at the specified path.
-# only. For editing an existing file, prefer fs_patch. It's a surgical edit that preserves unchanged content, requires
-# sending less data, and is less prone to accidental data loss.
 
 # @option --path! The path of the file to write to
 # @option --contents! The full contents to write to the file
@@ -15,9 +13,6 @@ source "$LLM_PROMPT_UTILS_FILE"
 
 # shellcheck disable=SC2154
 main() {
-    argc_contents="$(jq -r '.contents' <<< "$LLM_TOOL_RAW_JSON")"
-    argc_path="$(jq -r '.path' <<< "$LLM_TOOL_RAW_JSON")"
-
     if [[ -f "$argc_path" ]]; then
         printf "%s" "$argc_contents" | git diff --no-index "$argc_path" - || true
         guard_operation "Apply changes?"

assets/functions/tools/query_jira_issues.sh

@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+set -e
+
+# @meta require-tools jira
+# @describe Query for jira issues using a Jira Query Language (JQL) query
+# @option --jql-query! The Jira Query Language query to execute
+# @env LLM_OUTPUT=/dev/stdout The output path
+
+main() {
+  jira issue ls -q "$argc_jql_query" --plain >> "$LLM_OUTPUT"
+}

assets/functions/tools/send_mail.sh

@@ -14,10 +14,6 @@ set -e
 
 # shellcheck disable=SC2154
 main() {
-    argc_recipient="$(jq -r '.recipient' <<< "$LLM_TOOL_RAW_JSON")"
-    argc_subject="$(jq -r '.subject' <<< "$LLM_TOOL_RAW_JSON")"
-    argc_body="$(jq -r '.body' <<< "$LLM_TOOL_RAW_JSON")"
-
     sender_name="${EMAIL_SENDER_NAME:-$(echo "$EMAIL_SMTP_USER" | awk -F'@' '{print $1}')}"
     printf "%s\n" "From: $sender_name <$EMAIL_SMTP_USER>
 To: $argc_recipient 

assets/functions/tools/web_search_loki.sh

@@ -6,11 +6,11 @@ set -e
 
 # @option --query! The search query.
 
-# @meta require-tools coyote
+# @meta require-tools loki
 
 # @env WEB_SEARCH_MODEL=gemini:gemini-2.5-flash The model for web-searching.
 #
-# supported coyote models:
+# supported loki models:
 #   - gemini:gemini-2.0-*
 #   - vertexai:gemini-*
 #   - perplexity:*
@@ -22,15 +22,15 @@ main() {
     client="${WEB_SEARCH_MODEL%%:*}"
 
     if [[ "$client" == "gemini" ]]; then
-        export COYOTE_PATCH_GEMINI_CHAT_COMPLETIONS='{".*":{"body":{"tools":[{"google_search":{}}]}}}'
+        export LOKI_PATCH_GEMINI_CHAT_COMPLETIONS='{".*":{"body":{"tools":[{"google_search":{}}]}}}'
     elif [[ "$client" == "vertexai" ]]; then
-        export COYOTE_PATCH_VERTEXAI_CHAT_COMPLETIONS='{
+        export LOKI_PATCH_VERTEXAI_CHAT_COMPLETIONS='{
     "gemini-1.5-.*":{"body":{"tools":[{"googleSearchRetrieval":{}}]}},
     "gemini-2.0-.*":{"body":{"tools":[{"google_search":{}}]}}
 }'
     elif [[ "$client" == "ernie" ]]; then
-        export COYOTE_PATCH_ERNIE_CHAT_COMPLETIONS='{".*":{"body":{"web_search":{"enable":true}}}}'
+        export LOKI_PATCH_ERNIE_CHAT_COMPLETIONS='{".*":{"body":{"web_search":{"enable":true}}}}'
     fi
 
-    coyote -m "$WEB_SEARCH_MODEL" "$argc_query" >> "$LLM_OUTPUT"
+    loki -m "$WEB_SEARCH_MODEL" "$argc_query" >> "$LLM_OUTPUT"
 }

assets/functions/utils/prompt-utils.sh

@@ -506,16 +506,16 @@ open_link() {
 }
 
 guard_operation() {
-	if [[ -z "$AUTO_CONFIRM" && -z "$LLM_AGENT_VAR_AUTO_CONFIRM" ]]; then
+  if [[ -t 1 ]]; then
-		# 2>/dev/tty: keep the prompt off the host-captured stderr pipe so it
+  	if [[ -z "$AUTO_CONFIRM" && -z "$LLM_AGENT_VAR_AUTO_CONFIRM" ]]; then
-		# can't leak into tool_call_error JSON when the wrapped command fails.
+			ans="$(confirm "${1:-Are you sure you want to continue?}")"
-		ans="$(confirm "${1:-Are you sure you want to continue?}" 2>/dev/tty)"
 
-		if [[ "$ans" == 0 ]]; then
+			if [[ "$ans" == 0 ]]; then
-			error "Operation aborted!" 2>&1
+				error "Operation aborted!" 2>&1
-			exit 1
+				exit 1
+			fi
 		fi
-	fi
+  fi
 }
 
 # Here is an example of a patch block that can be applied to modify the file to request the user's name:
@@ -600,14 +600,6 @@ patch_file() {
 
           for (i = 2; i <= hunkTotalOriginalLines[hunkIndex]; i++) {
             if (lines[nextLineIndex] != hunkOriginalLines[hunkIndex,i]) {
-              if (i - 1 > bestPartialLen[hunkIndex]) {
-                bestPartialLen[hunkIndex] = i - 1
-                bestPartialAnchorLine[hunkIndex] = lineIndex
-                bestPartialHunkPos[hunkIndex] = i
-                bestPartialDivergeLine[hunkIndex] = nextLineIndex
-                bestPartialExpected[hunkIndex] = hunkOriginalLines[hunkIndex,i]
-                bestPartialActual[hunkIndex] = lines[nextLineIndex]
-              }
               nextLineIndex = 0
               break
             }
@@ -629,32 +621,7 @@ patch_file() {
         }
 
         if (hunkIndex != totalHunks + 1) {
-          failingHunk = hunkIndex
           print "error: unable to apply patch" > "/dev/stderr"
-          print "" > "/dev/stderr"
-          print "Hunk " failingHunk " of " totalHunks " did not match the file." > "/dev/stderr"
-
-          if (bestPartialLen[failingHunk] == 0) {
-            print "" > "/dev/stderr"
-            print "The first context/removed line of hunk " failingHunk " was not found anywhere in the file:" > "/dev/stderr"
-            print "  expected: " hunkOriginalLines[failingHunk, 1] > "/dev/stderr"
-          } else {
-            print "" > "/dev/stderr"
-            print "Closest match: anchored at file line " bestPartialAnchorLine[failingHunk] ", matched " bestPartialLen[failingHunk] " of " hunkTotalOriginalLines[failingHunk] " original lines before diverging." > "/dev/stderr"
-            print "" > "/dev/stderr"
-            print "At file line " bestPartialDivergeLine[failingHunk] " (hunk original line " bestPartialHunkPos[failingHunk] "):" > "/dev/stderr"
-            print "  expected: " bestPartialExpected[failingHunk] > "/dev/stderr"
-            print "  actual:   " bestPartialActual[failingHunk] > "/dev/stderr"
-          }
-
-          print "" > "/dev/stderr"
-          print "Lines must match byte-for-byte (no fuzzy matching). Check escaping, whitespace, and quoting." > "/dev/stderr"
-
-          if (failingHunk < totalHunks) {
-            print "" > "/dev/stderr"
-            print (totalHunks - failingHunk) " subsequent hunk(s) were not attempted (patcher aborts on first failure)." > "/dev/stderr"
-          }
-
           exit 1
         }
     }
@@ -688,18 +655,19 @@ guard_path() {
     exit 1
   fi
 
-	path="$(_to_real_path "$1")"
+  if [[ -t 1 ]]; then
-	confirmation_prompt="$2"
+    path="$(_to_real_path "$1")"
+    confirmation_prompt="$2"
 
-	if [[ ! "$path" == "$(pwd)"* && -z "$AUTO_CONFIRM" && -z "$LLM_AGENT_VAR_AUTO_CONFIRM" ]]; then
+    if [[ ! "$path" == "$(pwd)"* && -z "$AUTO_CONFIRM" && -z "$LLM_AGENT_VAR_AUTO_CONFIRM" ]]; then
-		# 2>/dev/tty: see guard_operation — prevents prompt text leaking via captured stderr.
+			ans="$(confirm "$confirmation_prompt")"
-		ans="$(confirm "$confirmation_prompt" 2>/dev/tty)"
 
-		if [[ "$ans" == 0 ]]; then
+			if [[ "$ans" == 0 ]]; then
-			error "Operation aborted!" >&2
+				error "Operation aborted!" >&2
-			exit 1
+				exit 1
-		fi
+			fi
-	fi
+    fi
+  fi
 }
 
 _to_real_path() {

assets/roles/atlassian.md

@@ -1,8 +0,0 @@
----
-enabled_mcp_servers: atlassian
----
-You are the librarian for the company's Confluence and Jira knowledge bases. Your job is to help users find and retrieve
-information from these platforms. Use all tools at your disposal to answer user queries.
-
-Available Tools:
-{{__tools__}}

assets/roles/create-prompt.md

@@ -1,6 +1,3 @@
----
-skills_enabled: false
----
 As a professional Prompt Engineer, your role is to create effective and innovative prompts for interacting with AI models.
 
 Your core skills include:

assets/roles/create-title.md

@@ -1,6 +1,3 @@
----
-skills_enabled: false
----
 Create a concise, 3-6 word title.
 
 **Notes**:

assets/roles/explain-shell.md

@@ -1,6 +1,3 @@
----
-skills_enabled: false
----
 Provide a terse, single sentence description of the given shell command.
 Describe each argument and option of the command.
 Provide short responses in about 80 words.

assets/roles/repo-analyzer.md

@@ -9,7 +9,7 @@ security/configuration settings. The analysis aims to ensure a thorough understa
 structured and operates, enabling the creation of new files, maintaining consistency with existing practices, and the
 potential implementation of best practices.
 
-Should the root directory contain a `COYOTE.md` file, this was generated by Coyote and should be used as a reference
+Should the root directory contain a `LOKI.md` file, this was generated by Loki and should be used as a reference
 point for all analysis, style questions, etc.
 
 **Objective:** Enable the AI to thoroughly analyze a software repository, providing detailed insights and guidelines on

assets/roles/shell.md

@@ -1,6 +1,3 @@
----
-skills_enabled: false
----
 Provide only {{__shell__}} commands for {{__os_distro__}} without any description.
 Ensure the output is a valid {{__shell__}} command.
 If there is a lack of details, provide most logical solution.

assets/roles/slack.md

@@ -1,5 +1,6 @@
 ---
 enabled_mcp_servers: slack
+temperature: 0.2
 ---
 You are an expert Slack assistant designed to assist with Slack workspaces via the slack MCP server. 
 You can perform various tasks related to Slack, such as sending messages to channels, searching for messages, and 

assets/sbx-kit/spec.yaml

@@ -1,326 +0,0 @@
-# Docker sbx agent kit for Coyote
-#
-# Setup (paths use $HOME so commands work in bash/zsh/PowerShell/Git Bash):
-#   sbx create --kit ./sbx-kit/ coyote --name testing .
-#   sbx cp $HOME/.config/coyote/ testing:/home/agent/.config/
-#   sbx cp $HOME/.coyote_password testing:/home/agent/
-#   sbx run testing --kit ./sbx-kit/
-schemaVersion: "1"
-kind: agent
-name: coyote
-displayName: Coyote
-description: >
-  An all-in-one, batteries-included LLM CLI tool featuring Shell Assistant,
-  CLI & REPL mode, RAG, AI tools & agents, MCP servers, skills, and macros.
-
-agent:
-  image: "docker/sandbox-templates:shell-docker"
-  aiFilename: COYOTE.md
-#  persistence: persistent
-  entrypoint:
-    run: ["bash", "-lc", "exec /home/agent/.cargo/bin/coyote"]
-
-network:
-  # Proxy-managed LLM providers: the proxy substitutes `proxy-managed` for
-  # the env var inside the sandbox and rewrites the auth header per
-  # serviceAuth at request time. Multiple domains may map to one service
-  # (e.g. jina) so they share a single credential.
-  serviceDomains:
-    api.openai.com: openai
-    api.anthropic.com: anthropic
-    generativelanguage.googleapis.com: gemini
-    api.cohere.ai: cohere
-    api.groq.com: groq
-    openrouter.ai: openrouter
-    api.ai21.com: ai21
-    api.cloudflare.com: cloudflare
-    api.deepinfra.com: deepinfra
-    api.deepseek.com: deepseek
-    api.mistral.ai: mistral
-    api.perplexity.ai: perplexity
-    api.voyageai.com: voyageai
-    api.x.ai: xai
-    api.jina.ai: jina
-    r.jina.ai: jina
-    qianfan.baidubce.com: ernie
-    api.hunyuan.cloud.tencent.com: hunyuan
-    api.minimax.chat: minimax
-    api.moonshot.cn: moonshot
-    dashscope.aliyuncs.com: qianwen
-    open.bigmodel.cn: zhipuai
-  serviceAuth:
-    openai:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    anthropic:
-      headerName: x-api-key
-      valueFormat: "%s"
-    gemini:
-      headerName: x-goog-api-key
-      valueFormat: "%s"
-    cohere:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    groq:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    openrouter:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    ai21:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    cloudflare:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    deepinfra:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    deepseek:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    mistral:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    perplexity:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    voyageai:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    xai:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    jina:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    ernie:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    hunyuan:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    minimax:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    moonshot:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    qianwen:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-    zhipuai:
-      headerName: Authorization
-      valueFormat: "Bearer %s"
-  allowedDomains:
-    # Coyote release + self-update + model-registry sync
-    - "github.com:443"
-    - "api.github.com:443"
-    - "raw.githubusercontent.com:443"
-    - "objects.githubusercontent.com:443"
-    - "*.githubusercontent.com:443"
-    # Coyote install paths (cargo install + uv + rustup + Python tool deps at runtime)
-    - "crates.io:443"
-    - "static.crates.io:443"
-    - "pypi.org:443"
-    - "files.pythonhosted.org:443"
-    - "astral.sh:443"
-    - "sh.rustup.rs:443"
-    - "static.rust-lang.org:443"
-
-    # LLM model OAuth + API endpoints
-    - "claude.ai:443"
-    - "console.anthropic.com:443"
-    - "accounts.google.com:443"
-    # *.googleapis.com covers oauth2 + userinfo + VertexAI regional endpoints
-    # (*-aiplatform.googleapis.com). Do not narrow without re-checking VertexAI.
-    - "*.googleapis.com:443"
-
-    # Bedrock and GitHub Models use signed / GitHub-PAT auth that the proxy
-    # cannot rewrite. Domains are allow-listed; credentials must be injected
-    # separately (see README "Extending").
-    - "*.amazonaws.com:443"
-    - "models.inference.ai.azure.com:443"
-
-credentials:
-  sources:
-    openai:
-      env:
-        - OPENAI_API_KEY
-    anthropic:
-      env:
-        - ANTHROPIC_API_KEY
-    gemini:
-      env:
-        - GEMINI_API_KEY
-        - GOOGLE_API_KEY
-    cohere:
-      env:
-        - COHERE_API_KEY
-    groq:
-      env:
-        - GROQ_API_KEY
-    openrouter:
-      env:
-        - OPENROUTER_API_KEY
-    ai21:
-      env:
-        - AI21_API_KEY
-    cloudflare:
-      env:
-        - CLOUDFLARE_API_KEY
-    deepinfra:
-      env:
-        - DEEPINFRA_API_KEY
-    deepseek:
-      env:
-        - DEEPSEEK_API_KEY
-    mistral:
-      env:
-        - MISTRAL_API_KEY
-    perplexity:
-      env:
-        - PERPLEXITY_API_KEY
-    voyageai:
-      env:
-        - VOYAGE_API_KEY
-    xai:
-      env:
-        - XAI_API_KEY
-    jina:
-      env:
-        - JINA_API_KEY
-    ernie:
-      env:
-        - ERNIE_API_KEY
-    hunyuan:
-      env:
-        - HUNYUAN_API_KEY
-    minimax:
-      env:
-        - MINIMAX_API_KEY
-    moonshot:
-      env:
-        - MOONSHOT_API_KEY
-    qianwen:
-      env:
-        - DASHSCOPE_API_KEY
-    zhipuai:
-      env:
-        - ZHIPUAI_API_KEY
-
-environment:
-  variables:
-    IS_SANDBOX: "1"
-    COYOTE_LOG_LEVEL: INFO
-  proxyManaged:
-    - OPENAI_API_KEY
-    - ANTHROPIC_API_KEY
-    - GEMINI_API_KEY
-    - GOOGLE_API_KEY
-    - COHERE_API_KEY
-    - GROQ_API_KEY
-    - OPENROUTER_API_KEY
-    - AI21_API_KEY
-    - CLOUDFLARE_API_KEY
-    - DEEPINFRA_API_KEY
-    - DEEPSEEK_API_KEY
-    - MISTRAL_API_KEY
-    - PERPLEXITY_API_KEY
-    - VOYAGE_API_KEY
-    - XAI_API_KEY
-    - JINA_API_KEY
-    - ERNIE_API_KEY
-    - HUNYUAN_API_KEY
-    - MINIMAX_API_KEY
-    - MOONSHOT_API_KEY
-    - DASHSCOPE_API_KEY
-    - ZHIPUAI_API_KEY
-
-commands:
-  install:
-    - command: |
-        sudo apt-get update &&
-        sudo apt-get install -y \
-          jq curl git \
-          build-essential pkg-config \
-          cmake \
-          clang libclang-dev \
-          musl-tools \
-          libssl-dev \
-          pandoc \
-          bzip2
-      user: "1000"
-      description: Install system prerequisites (including pandoc for fetch_url_via_curl)
-    - command: "curl -LsSf https://astral.sh/uv/install.sh | sh"
-      user: "1000"
-      description: Install uv (required for Python-based custom tools)
-    - command: |
-        set -euo pipefail
-        USQL_VERSION=$(curl -sSL https://api.github.com/repos/xo/usql/releases/latest | jq -r .tag_name | sed 's/^v//')
-        ARCH=$(uname -m)
-        case "$ARCH" in
-          x86_64) USQL_ARCH=amd64 ;;
-          aarch64) USQL_ARCH=arm64 ;;
-          *) echo "Unsupported arch for usql install: $ARCH" >&2; exit 1 ;;
-        esac
-        TMPDIR=$(mktemp -d)
-        trap 'rm -rf "$TMPDIR"' EXIT
-        curl -sSL "https://github.com/xo/usql/releases/download/v${USQL_VERSION}/usql_static-${USQL_VERSION}-linux-${USQL_ARCH}.tar.bz2" -o "$TMPDIR/usql.tar.bz2"
-        tar -xjf "$TMPDIR/usql.tar.bz2" -C "$TMPDIR"
-        sudo install -m 0755 "$TMPDIR/usql_static" /usr/local/bin/usql
-      user: "1000"
-      description: Install the usql universal SQL CLI (used by the built-in sql agent and execute_sql_code tool)
-    - command: |
-        curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | \
-          sh -s -- -y \
-          --default-toolchain stable \
-          --profile minimal \
-          --target x86_64-unknown-linux-musl
-        . "$HOME/.cargo/env"
-        cargo install --locked coyote-ai
-      user: "1000"
-      description: Install Coyote AI CLI via Rust's Cargo
-
-  startup:
-    - command: ["sh", "-c", "test -f \"$HOME/.config/coyote/config.yaml\" || coyote --info >/dev/null 2>&1 || true"]
-      user: "1000"
-      background: false
-      description: Bootstrap Coyote config directory on first sandbox start
-
-memory: |
-  ## Sandbox environment
-
-  You are running inside a Docker sandbox launched via `sbx run coyote`. The
-  user's project workspace is mounted at its absolute host path and is the
-  current working directory. `sudo` is passwordless; use it for system
-  package installs.
-
-  Coyote's configuration lives at `~/.config/coyote/` and logs at
-  `~/.cache/coyote/coyote.log`. Persistence is enabled, so config, sessions,
-  vault state, OAuth tokens, and installed tools survive sandbox restarts.
-
-  LLM provider credentials are forwarded by the sandbox HTTP proxy. The
-  following provider env vars are recognized - export the ones you use on
-  the host before running `sbx run coyote`:
-
-    OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY / GOOGLE_API_KEY,
-    COHERE_API_KEY, GROQ_API_KEY, OPENROUTER_API_KEY, AI21_API_KEY,
-    CLOUDFLARE_API_KEY, DEEPINFRA_API_KEY, DEEPSEEK_API_KEY,
-    MISTRAL_API_KEY, PERPLEXITY_API_KEY, VOYAGE_API_KEY, XAI_API_KEY,
-    JINA_API_KEY, ERNIE_API_KEY, HUNYUAN_API_KEY, MINIMAX_API_KEY, 
-    MOONSHOT_API_KEY, DASHSCOPE_API_KEY (Qwen), ZHIPUAI_API_KEY
-
-  Inside the sandbox these appear as the placeholder string `proxy-managed`;
-  the proxy substitutes the real value at request time. OAuth flows for
-  Claude Pro/Max and Gemini are also allow-listed.
-
-  Bedrock (AWS) and VertexAI (Google Cloud) use signed/OAuth-token requests
-  that the proxy cannot rewrite. Their domains are allow-listed but you must
-  inject credentials yourself via `sbx run --env AWS_ACCESS_KEY_ID=...` or
-  a mixin kit that mounts a service-account JSON.
-
-  Useful first-run commands:
-  - `coyote --info`          # show config paths and resolved settings
-  - `coyote --list-secrets`  # initialise the local vault
-  - `coyote --authenticate <client>`  # OAuth flow (Claude Pro/Max, Gemini)

assets/sbx-vault-mixins/aws_secrets_manager/spec.yaml

@@ -1,33 +0,0 @@
-schemaVersion: "1"
-kind: mixin
-name: vault-aws-secrets-manager
-description: >
-  Installs the AWS CLI v2 so the Coyote vault can read secrets from AWS
-  Secrets Manager inside the sandbox. The AWS Rust SDK does not strictly
-  require the CLI, but most users authenticate via `aws sso login` or
-  `aws configure`, which need the CLI to be installed. After install, run
-  the appropriate auth command in the sandbox; cached credentials persist
-  for the lifetime of the sandbox.
-
-network:
-  allowedDomains:
-    - "awscli.amazonaws.com:443"
-    - "sts.amazonaws.com:443"
-    - "*.sts.amazonaws.com:443"
-    - "*.secretsmanager.amazonaws.com:443"
-    - "*.amazonaws.com:443"
-    - "*.awsapps.com:443"
-
-commands:
-  install:
-    - command: |
-        set -euo pipefail
-        sudo apt-get update
-        sudo apt-get install -y unzip
-        ARCH=$(uname -m)
-        curl -sSL "https://awscli.amazonaws.com/awscli-exe-linux-${ARCH}.zip" -o /tmp/awscliv2.zip
-        unzip -q /tmp/awscliv2.zip -d /tmp
-        sudo /tmp/aws/install
-        rm -rf /tmp/awscliv2.zip /tmp/aws
-      user: "1000"
-      description: Install AWS CLI v2 from the official installer

assets/sbx-vault-mixins/azure_key_vault/spec.yaml

@@ -1,24 +0,0 @@
-schemaVersion: "1"
-kind: mixin
-name: vault-azure-key-vault
-description: >
-  Installs the Azure CLI (`az`) so the Coyote vault can read secrets from
-  Azure Key Vault inside the sandbox. After install, run `az login` in the
-  sandbox to authenticate; the session token persists for the lifetime of
-  the sandbox.
-
-network:
-  allowedDomains:
-    - "aka.ms:443"
-    - "packages.microsoft.com:443"
-    - "azurecliprod.blob.core.windows.net:443"
-    - "login.microsoftonline.com:443"
-    - "graph.microsoft.com:443"
-    - "management.azure.com:443"
-    - "*.vault.azure.net:443"
-
-commands:
-  install:
-    - command: "curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash"
-      user: "1000"
-      description: Install Azure CLI via Microsoft's official install script

assets/sbx-vault-mixins/gcp_secret_manager/spec.yaml

@@ -1,34 +0,0 @@
-schemaVersion: "1"
-kind: mixin
-name: vault-gcp-secret-manager
-description: >
-  Installs the Google Cloud CLI (`gcloud`) so the Coyote vault can read
-  secrets from GCP Secret Manager inside the sandbox. The GCP Rust SDK does
-  not strictly require the CLI, but most users authenticate via
-  `gcloud auth application-default login`, which needs the CLI to be
-  installed. After install, run that command in the sandbox; the ADC file
-  persists for the lifetime of the sandbox.
-
-network:
-  allowedDomains:
-    - "packages.cloud.google.com:443"
-    - "accounts.google.com:443"
-    - "oauth2.googleapis.com:443"
-    - "secretmanager.googleapis.com:443"
-    - "cloudresourcemanager.googleapis.com:443"
-    - "*.googleapis.com:443"
-
-commands:
-  install:
-    - command: |
-        set -euo pipefail
-        sudo apt-get update
-        sudo apt-get install -y apt-transport-https ca-certificates gnupg
-        echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main" \
-          | sudo tee /etc/apt/sources.list.d/google-cloud-sdk.list >/dev/null
-        curl -sSL https://packages.cloud.google.com/apt/doc/apt-key.gpg \
-          | sudo gpg --dearmor -o /usr/share/keyrings/cloud.google.gpg
-        sudo apt-get update
-        sudo apt-get install -y google-cloud-cli
-      user: "1000"
-      description: Install gcloud CLI from Google's official apt repository

assets/sbx-vault-mixins/gopass/spec.yaml

@@ -1,30 +0,0 @@
-schemaVersion: "1"
-kind: mixin
-name: vault-gopass
-description: >
-  Installs `gopass` and `gpg` so the Coyote vault can read secrets from a
-  gopass store inside the sandbox. The store must be cloned manually
-  (gopass walks a user-specific git remote, so v1 only allowlists github.com
-  and gitlab.com; add other hosts via a user mixin if needed). After install,
-  run `gopass setup` or `gopass clone <remote>` in the sandbox.
-
-network:
-  allowedDomains:
-    - "github.com:443"
-    - "api.github.com:443"
-    - "objects.githubusercontent.com:443"
-    - "gitlab.com:443"
-
-commands:
-  install:
-    - command: |
-        set -euo pipefail
-        sudo apt-get update
-        sudo apt-get install -y gnupg2 git
-        GOPASS_VERSION="1.15.13"
-        ARCH=$(dpkg --print-architecture)
-        curl -sSL "https://github.com/gopasspw/gopass/releases/download/v${GOPASS_VERSION}/gopass_${GOPASS_VERSION}_linux_${ARCH}.deb" -o /tmp/gopass.deb
-        sudo dpkg -i /tmp/gopass.deb
-        rm -f /tmp/gopass.deb
-      user: "1000"
-      description: Install gnupg2, git, and gopass from the official .deb release

assets/sbx-vault-mixins/one_password/spec.yaml

@@ -1,31 +0,0 @@
-schemaVersion: "1"
-kind: mixin
-name: vault-one-password
-description: >
-  Installs the 1Password CLI (`op`) so the Coyote vault can decrypt secrets
-  inside the sandbox. After install, run `op signin` in the sandbox to
-  authenticate; credentials persist for the lifetime of the sandbox.
-
-network:
-  allowedDomains:
-    - "downloads.1password.com:443"
-    - "cache.agilebits.com:443"
-    - "my.1password.com:443"
-    - "my.1password.eu:443"
-    - "my.1password.ca:443"
-    - "events.1password.com:443"
-
-commands:
-  install:
-    - command: |
-        set -euo pipefail
-        sudo apt-get update
-        sudo apt-get install -y unzip
-        OP_VERSION="v2.30.3"
-        ARCH=$(dpkg --print-architecture)
-        curl -sSL "https://cache.agilebits.com/dist/1P/op2/pkg/${OP_VERSION}/op_linux_${ARCH}_${OP_VERSION}.zip" -o /tmp/op.zip
-        sudo unzip -od /usr/local/bin /tmp/op.zip op
-        sudo chmod +x /usr/local/bin/op
-        rm -f /tmp/op.zip
-      user: "1000"
-      description: Install 1Password CLI from the official archive

assets/skills/ai-slop-remover/SKILL.md

@@ -1,39 +0,0 @@
----
-description: Detect and remove AI slop from code and prose; produce output indistinguishable from a senior engineer's.
----
-You are reviewing or generating content. Apply these standards strictly. The goal is output that reads like it was written by a competent human professional, not an AI.
-
-## Code
-
-**No useless comments.** A comment is useless if it restates the code:
-- BAD: `// Increment counter` above `counter += 1`
-- BAD: `/// Returns the user's name.` on `fn user_name() -> &str`
-- GOOD: Comments that explain a non-obvious WHY: a constraint, an invariant, a workaround for a specific bug, behavior that would surprise a reader.
-
-If removing a comment wouldn't confuse a future reader, the comment shouldn't exist.
-
-**No emojis** unless the user explicitly asked for them.
-
-**No defensive handling for impossible cases.** If a function only receives valid input from internal callers, don't pretend otherwise. Validate at system boundaries (user input, external APIs, file I/O); trust internal code.
-
-**No over-engineering for hypothetical futures.** Three similar lines of code is fine. Premature abstractions are worse than duplication.
-
-**No backwards-compatibility cruft for unreleased code.** If a function isn't called yet, just change it. Don't add `_unused` prefixes, "// removed" comments, or wrapper layers "for migration."
-
-**Names should be honest.** A function called `get_user` should not mutate state. A field called `count` should not be a function. A method that can fail should return `Result`, not panic.
-
-## Prose
-
-**No flattery.** Don't start with "Great question!" or "That's a really good idea!" Just respond.
-
-**No filler.** "It's important to note that" — delete. "Let me explain" — just explain. "I'll go ahead and" — just do it.
-
-**No status updates.** "I'm going to help you with that" — just help.
-
-**Match the user's terseness.** Brief user, brief reply. Detailed user, detailed reply.
-
-**No multi-paragraph docstrings.** One short line max. If the function needs paragraphs to explain, the function is doing too much.
-
-## When in doubt
-
-Ask: "Would a senior engineer write this in a code review or a Slack message?" If not, cut it.

assets/skills/code-review/SKILL.md

@@ -1,124 +0,0 @@
----
-description: Conduct a thorough code review focused on correctness, clarity, tests, and footguns. Grants read-only filesystem access for inspecting code.
-enabled_tools: fs_read, fs_grep, fs_glob, fs_cat, fs_ls
----
-You are reviewing code. Use the filesystem tools (`fs_read`, `fs_grep`, `fs_glob`, `fs_cat`, `fs_ls`) to inspect files. Apply this checklist in order; stop at the first category where you find substantial issues, since fixing those usually shifts the rest of the review.
-
-## Investigation workflow
-
-Before reviewing, build a mental model of the surrounding code:
-
-- `fs_ls` the directories that contain the changed files.
-- `fs_grep` for the symbols being added/modified to see existing callers and tests.
-- `fs_read` neighboring files in the same module to understand local conventions.
-- `fs_glob` for test files that might cover this area.
-
-A review without context is just a syntax check.
-
-## Reviewing a diff
-
-When you only see a hunk (not the whole file), the default context is sparse — usually 3 lines on either side. You see what changed but rarely the function signature, the caller, or the test. Read deliberately to recover what the diff omits.
-
-### Read around the hunk
-
-The `@@ -120,8 +120,12 @@` header gives you the line numbers in the old (`-`) and new (`+`) file. Read 20–40 lines around the hunk to see the enclosing function:
-
-```
-fs_read --path "src/auth.rs" --offset 110 --limit 40
-```
-
-You're recovering: the function signature, the return type, what unchanged portions do, and whether the hunk's logic fits its enclosing scope.
-
-### Read the callers of anything changed
-
-If a hunk changes a function's body or its signature, grep for the name to find callers and check whether the change ripples:
-
-```
-fs_grep --pattern "changed_function" --include "*.rs"
-```
-
-Skip the test files in this search; do the test sweep next.
-
-### Read the tests for the change
-
-Even if the diff doesn't touch test files, check whether tests exist for what's changing:
-
-```
-fs_grep --pattern "changed_function" --include "*_test.rs"
-fs_grep --pattern "changed_function" --include "tests/*"
-```
-
-Absence of tests for a changed function is itself a finding ("changes function X but no test references it; regressions won't be caught").
-
-### Diff-shaped issues to watch for
-
-These are review findings that only surface in a diff context, not in a whole-file read:
-
-- **Renames** (`diff --git a/old.rs b/new.rs`) — `fs_grep` for the old path to find imports that need updating but weren't.
-- **Signature changes** — verify all callers compile against the new signature. Compiler-checked languages catch some of this; dynamic languages don't.
-- **New code path without new tests** — usually a missing test. Flag it.
-- **Removed code with tests still present** — the tests probably need updating too.
-- **The "dog that didn't bark"** — what's obvious by its ABSENCE? A new field with no migration, a new error path with no test, a public API change with no changelog, a new config option with no documentation. Flag these as missing pieces, not as things to add later.
-
-### Scope discipline
-
-A diff review is a review of THE CHANGE, not the whole file:
-
-- Don't moralize about pre-existing code unless the diff makes it worse.
-- Don't suggest refactors outside the scope of the change. ("This whole module could be cleaner" is not actionable feedback on a 5-line patch.)
-- If you spot unrelated bugs while reading context, mention them briefly but separately: prefix with `Pre-existing, out of scope:` so the author knows which findings block their merge and which are FYI.
-- The author's job is to ship THIS change. Your job is to catch what's wrong with THIS change.
-
-## 1. Correctness
-
-- Does the change actually do what it claims? Does it solve the stated problem?
-- Edge cases: empty inputs, max sizes, concurrent access, error paths, partial failures.
-- Off-by-one errors, type confusion, null/None handling, integer overflow.
-- Race conditions and ordering assumptions across threads, async tasks, or distributed components.
-- Resource cleanup: file handles, locks, network connections, transactions.
-
-## 2. Tests
-
-- Do the tests test BEHAVIOR, not implementation? (Tests of `private_helper()` are usually a smell.)
-- Will they fail when the code regresses? Or are they tautological (e.g., `assert!(x.is_empty() || !x.is_empty())`)?
-- Do they cover the unhappy paths, not just the happy ones?
-- Is there a missing test for the specific bug or feature being added? `fs_grep` for the function name in test files to check.
-
-## 3. Clarity
-
-- Are names accurate? `get_user` that mutates is a lie; rename or split.
-- Could a competent reader understand this without comments?
-- Is there a simpler way to express the same logic?
-- Is the function doing one thing, or several things glued together?
-
-## 4. Coupling
-
-- Does this change increase coupling between modules unnecessarily?
-- Is the new code reaching into internals it shouldn't (private fields exposed, deep import paths)?
-- Could the change be expressed as a smaller diff that doesn't ripple through unrelated files?
-
-## 5. Footguns
-
-- Could a future maintainer easily misuse this API?
-- Are invariants enforced by types, or just by convention?
-- Are error types specific enough to be actionable?
-- Is there a documented or implicit ordering requirement that's easy to break?
-
-## What to flag
-
-- Correctness bugs.
-- Missing error handling at trust boundaries.
-- Race conditions.
-- Tests that won't catch regressions.
-- Security issues (injection, auth, exposed secrets).
-
-## What to let go
-
-- Style differences that aren't in the codebase's existing conventions.
-- "I would have done it differently" preferences.
-- Comments and naming choices that match existing patterns in the same file.
-- Micro-optimizations in code that isn't on a hot path.
-
-## Tone
-
-Direct, specific, focused on the code. No flattery, no padding. If something is wrong, say so plainly with the file path and line reference and the reason. If something is good and non-obvious, briefly call it out so the author knows it's intentional.

assets/skills/delegation-protocol/SKILL.md

@@ -1,69 +0,0 @@
----
-description: Structured 6-section delegation template and session-continuity rules for orchestrating sub-agents. Load before spawning any agent.
----
-You are delegating work to a sub-agent. The sub-agent has not seen the codebase or the conversation — your prompt IS its entire context. Treat delegation as writing a contract: explicit, scoped, and verifiable.
-
-## The 6-section template (every delegation)
-
-Every `agent__spawn` prompt MUST include all six sections. Vague prompts produce vague results and waste tokens on re-exploration the orchestrator already did.
-
-```
-## TASK
-[One atomic goal. One verb. One outcome. No "and also".]
-
-## EXPECTED OUTCOME
-[Concrete deliverables and success criteria. "I will know this is done when ..."]
-
-## REQUIRED TOOLS
-[Explicit allowlist: fs_read, fs_grep, etc. Prevents tool sprawl.]
-
-## MUST DO
-[Exhaustive requirements. Leave nothing implicit. If you'd be annoyed by the agent not doing X, list X.]
-
-## MUST NOT DO
-[Forbidden actions. Anticipate rogue behavior. "Do not modify files outside src/auth/."]
-
-## CONTEXT
-[File paths, code snippets, existing patterns, constraints. Paste actual code lines from prior exploration — not just file paths.]
-```
-
-## Session continuity (NON-NEGOTIABLE)
-
-Every `agent__spawn` result includes a session_id. **Use it.**
-
-- Task failed/incomplete → resume with `session_id` + a tight "Fix: <error>" prompt.
-- Follow-up on a result → resume with `session_id` + "Also: <question>".
-- Multi-turn with the same agent → always resume. Never start fresh.
-
-Starting a fresh agent for a follow-up forces it to re-read every file it already read. That's 70%+ wasted tokens, plus the agent loses the reasoning it built up.
-
-After every delegation, **store the session_id** for potential continuation.
-
-## Skill nudges to delegates
-
-Sub-agents have their own skills. Nudge them in the CONTEXT section:
-
-> "Load `code-review` before evaluating the diff."
-> "Load `frontend-ui-ux` before editing component files."
-> "Load `git-master` before touching history."
-
-A one-line nudge saves the delegate a `skill__list` turn.
-
-## Verification after delegation
-
-A delegation is NOT complete when the sub-agent returns. It is complete when YOU have verified:
-
-1. Did it work as expected? (Did the file change? Did the test pass?)
-2. Did it follow existing codebase patterns?
-3. Did the EXPECTED OUTCOME actually materialize?
-4. Did it respect MUST DO and MUST NOT DO?
-
-If any answer is no → resume the session with a corrective prompt. Do not re-spawn from scratch.
-
-## Anti-patterns
-
-- "Follow existing patterns" with no snippet → agent guesses, often wrong
-- Multi-goal prompts → agent does the easy one, skips the rest
-- Missing MUST NOT DO → agent over-reaches into unrelated files
-- Discarding session_id on failure → forced re-exploration, wasted tokens
-- Re-spawning instead of resuming for a 1-line fix → 10x cost

assets/skills/frontend-ui-ux/SKILL.md

@@ -1,67 +0,0 @@
----
-description: Designer-turned-developer who crafts stunning UI/UX even without design mockups. Grants filesystem read/write access for editing component files.
-enabled_tools: fs_read, fs_write, fs_patch, fs_grep, fs_glob, fs_cat, fs_ls, fs_mkdir
----
-You are doing frontend work. Use the filesystem tools to read, write, and patch component files. Treat UI/UX as a discipline, not a polish step at the end.
-
-## Investigate before editing
-
-Before changing a component:
-
-- `fs_ls` the component's directory to see siblings and tests.
-- `fs_read` the component itself.
-- `fs_grep` for the component's usages across the codebase — your edits affect every caller.
-- `fs_grep` for the project's design tokens, theme variables, or styling primitives (e.g., `--color-`, `theme.spacing`, `tw-`).
-- Read existing similar components to match conventions.
-
-## Visual hierarchy
-
-Every screen has a focal point. Identify it before laying out anything else:
-
-- One primary action per view. Make it visually dominant.
-- Secondary actions are present but visibly subordinate.
-- Tertiary actions can be tucked into menus or hidden behind affordances.
-
-## Spacing and rhythm
-
-- Use the project's existing spacing scale (4px, 8px, custom — match what's already there). Don't introduce one-off values.
-- Larger spacing = stronger grouping break. Inside a card, tight; between cards, looser.
-- White space is not wasted space. It's the difference between "professional" and "cramped."
-
-## Typography
-
-- Two or three sizes per view, max. More than that is noise.
-- Line-height: 1.4-1.6 for body, tighter for headlines.
-- Don't center long paragraphs. Left-align (or right-align for RTL).
-
-## Color
-
-- Use the project's existing palette. If you need a color that isn't there, you're probably overdesigning.
-- Contrast matters: aim for WCAG AA at minimum (4.5:1 for body text, 3:1 for large text).
-- Don't use color as the sole signal — pair with icons, labels, or shape changes for accessibility.
-
-## Component conventions
-
-When adding a new component:
-
-- Match the existing structure: where do props go, where do styles go, where do tests go?
-- `fs_read` two or three similar components first to internalize the patterns.
-- If the codebase uses CSS modules / styled-components / Tailwind / Vanilla Extract — use the same. Don't introduce a new system.
-- Co-locate tests and stories with the component, matching the existing convention.
-
-## Forms
-
-- Label every input. Placeholder text is not a label.
-- Show validation errors near the field, not in a banner at the top.
-- Validate on blur, not on every keystroke. Show success states only after the user has interacted.
-- Required fields: mark visually AND in the input's accessibility attributes.
-
-## Loading and empty states
-
-- Empty states are an opportunity, not a fallback. Tell the user what they can do, not "no data."
-- Loading: show structure (skeletons) when you know what's coming. Spinners are for indeterminate waits.
-- Errors: explain WHAT failed and what the user can do about it. "Something went wrong" is useless.
-
-## When unsure
-
-Ship the boring version. A well-executed boring design beats an under-executed clever one every time.

assets/skills/git-master/SKILL.md

@@ -1,58 +0,0 @@
----
-description: Methodology for atomic commits, rebase surgery, and clean git history. Grants shell access for running git commands.
-enabled_tools: execute_command
----
-You are operating on a git repository. Apply these conventions strictly. Use the `execute_command` tool to run git commands.
-
-## Atomic commits
-
-Each commit represents one logical change. If the commit message needs the word "and," the change is too large; split it. Mixed concerns in one commit are nearly impossible to revert cleanly later.
-
-## Commit messages
-
-- Subject line: imperative mood, ≤50 characters, no trailing period.
-- Blank line.
-- Body: explain WHY, not WHAT. The diff shows what changed.
-- Reference issues by URL or canonical ID, not by free-form description.
-
-## Rebase, don't merge
-
-- `git rebase -i origin/main` before opening a PR.
-- Squash WIP commits and fixups; keep only meaningful commits in the final history.
-- Never rebase a branch others may have based work on. If unsure, ask.
-
-## Conflict resolution
-
-- Read both sides carefully before resolving. Don't reflexively take "ours" or "theirs."
-- After resolving, run tests before continuing the rebase.
-- For non-trivial conflicts, document the resolution choice in the resulting commit body.
-
-## Investigation workflow
-
-Use `execute_command` to run these inspection commands when chasing down history:
-
-- `git log -p <file>` — see how a file evolved over time.
-- `git log -S '<string>'` (pickaxe) — find when a string was added or removed.
-- `git log --all --grep '<pattern>'` — search commit messages.
-- `git blame -L <start>,<end> <file>` — current authorship for a line range.
-- `git diff <ref1>..<ref2> -- <path>` — narrow diffs to specific paths.
-- `git bisect start && git bisect bad && git bisect good <ref>` — narrow down regressions.
-
-## Safety checklist before destructive operations
-
-Before running anything that rewrites history or deletes refs:
-
-- `git status` — confirm clean working tree.
-- `git branch --show-current` — confirm which branch you're on.
-- `git log -3 --oneline` — confirm what's about to be moved.
-
-## What to never do
-
-- Force-push to shared branches (`main`, release branches, anything teammates pull from).
-- `git reset --hard` without confirming current branch and verifying the reflog can recover.
-- `git push --no-verify` to skip hooks — fix the underlying issue instead.
-- Commit secrets, even temporarily. Once pushed, treat as compromised; rotate.
-
-## When unsure, read state first
-
-Before guessing at a fix, run `git status`, `git log -5 --oneline`, and `git diff` (or `git diff --staged`) to see the actual state. Don't operate on assumptions.

assets/skills/oracle-protocol/SKILL.md

@@ -1,81 +0,0 @@
----
-description: Discipline for when and how to consult Oracle - blocking by design, never deliver an answer with Oracle pending, never bypass Oracle for design questions.
----
-Oracle is your read-only, high-IQ advisor. Using it correctly is the difference between shipping the right thing slowly and shipping the wrong thing fast.
-
-## When you MUST consult Oracle
-
-Spawn `oracle` (do NOT answer yourself) any time the user asks:
-
-- "How should I..." / "What's the best way to..." — design/approach questions
-- "Why does X keep..." / "What's wrong with..." — complex debugging (not simple errors)
-- "Should I use X or Y?" — technology or pattern choices
-- "How should this be structured?" — architecture and organization
-- "Review this" / "What do you think of..." — code/design review
-- Tradeoff questions — performance vs readability, complexity vs flexibility
-- Multi-component questions — anything spanning 3+ files or modules
-- Vague/open-ended — "improve this", "make this better", "clean this up"
-- After 2+ failed fix attempts on the same problem — complex debugging
-
-Even if you think you know the answer, Oracle provides deeper, more thorough analysis. The only exception is truly trivial questions about a single file you've already read.
-
-## Oracle is BLOCKING by design
-
-The orchestrator (you) has paused work and CANNOT proceed until Oracle returns. This is intentional. The cost of Oracle's latency is paid so YOU get a thorough, considered answer rather than rushing in a wrong direction.
-
-Therefore:
-
-- **Do NOT implement before Oracle returns** if your implementation depends on Oracle's recommendation.
-- **Do NOT deliver the final user-facing answer** while Oracle is still running.
-- **Do NOT "time out and continue anyway"** for Oracle-dependent tasks.
-- While waiting, do only NON-OVERLAPPING prep work (work that doesn't depend on Oracle's verdict).
-
-## How to consult Oracle effectively
-
-Oracle has not seen the codebase or the conversation. Give it enough context to think:
-
-```
-## Question
-[The decision you need help with, stated as a question]
-
-## Background
-[Why this question matters now. What constraint or trigger raised it.]
-
-## Code context
-[Paste the actual snippets from prior exploration — file paths alone are not enough]
-- From `path/to/file.ext`:
-  <relevant 5-20 lines>
-
-## What you've considered
-[Options you've already weighed and their tradeoffs as you see them]
-
-## What I'd love Oracle to evaluate
-[Specific aspects: correctness, performance, security, future flexibility, etc.]
-```
-
-A well-scoped Oracle consult returns a tighter answer faster.
-
-## After Oracle returns
-
-1. Read the recommendation, reasoning, and risks sections carefully.
-2. If the recommendation conflicts with your prior plan, update the plan — do not silently ignore Oracle.
-3. Pass Oracle's recommendation (and reasoning) to the implementer (e.g., coder) as CONTEXT in your delegation.
-4. If you disagree with Oracle's verdict, raise it with the user before implementing the alternative — don't act unilaterally against Oracle's advice.
-
-## When NOT to consult Oracle
-
-- Simple file operations you can do with direct tools
-- First attempt at any fix (try yourself first; consult after 2 failures)
-- Questions answerable from code you've already read
-- Trivial decisions (variable names in small functions, formatting)
-- Things you can infer from existing code patterns
-
-Over-consultation wastes Oracle's budget and slows the work. Reserve Oracle for genuinely hard or load-bearing decisions.
-
-## Anti-patterns (BLOCKING)
-
-- Answering an architecture question yourself "just this once"
-- Delivering a user-facing answer while Oracle is still running
-- Implementing the obvious approach without consulting Oracle on a tradeoff question
-- Ignoring Oracle's recommendation because it's inconvenient
-- Polling `agent__collect` on a running Oracle (end your response, wait for notification)

assets/skills/parallel-research/SKILL.md

@@ -1,70 +0,0 @@
----
-description: Fan-out exploration protocol — fire multiple research agents in parallel, wait for completion notifications, and never duplicate delegated work.
----
-You are entering a research phase. Exploration is parallelizable; serial reads leave throughput on the table.
-
-## Fan out, don't read serially
-
-For any non-trivial codebase question, fire 2-5 `explore` agents in parallel, each scoped to a different angle:
-
-- Auth implementation? → one for routes, one for middleware, one for token handling, one for error response shape.
-- Bug investigation? → one for the failing path, one for similar working paths, one for recent changes near the area.
-
-Each agent gets a NARROW slice. Narrow scope = fast, focused result. Broad scope = the agent over-reads and returns a wall of text.
-
-## The wait protocol
-
-After spawning background agents:
-
-1. If you have **non-overlapping** work to do (work that doesn't depend on the delegated research), do it now.
-2. If you don't, **end your response.** Do not call `agent__collect` immediately — the agent is still running.
-3. The system notifies you when the agent completes (`pending_escalations` or completion event).
-4. On notification, call `agent__collect` to retrieve results.
-
-Polling `agent__collect` on a still-running agent blocks your turn for nothing.
-
-## Anti-duplication rule (BLOCKING)
-
-Once you delegate a search to an `explore` agent, **do not perform that same search yourself.**
-
-Forbidden:
-- After firing `explore` for "auth middleware", running `fs_grep` for "auth middleware" yourself
-- "Just quickly checking" the same files the delegate is checking
-- Re-doing the research while waiting impatiently
-
-Allowed:
-- Non-overlapping work in a different module
-- Preparation work that doesn't depend on the delegated result
-- Ending your response and waiting
-
-Duplicate searches waste tokens, may contradict the delegate, and defeat the point of parallelism.
-
-## Stop conditions
-
-Stop searching when:
-
-- The same information appears across multiple sources
-- Two search iterations yield no new useful data
-- A direct answer was found
-- You have enough context to proceed confidently
-
-Over-exploration is as bad as under-exploration. Time spent searching is time not spent shipping.
-
-## Parallel + sequential composition
-
-It is fine to fire `explore` and then `oracle` when oracle needs the explore results — just sequence them:
-
-1. Fire explore(s) in parallel.
-2. End response, wait for completion.
-3. Synthesize findings, fire `oracle` with those findings as CONTEXT.
-4. End response, wait for oracle.
-5. Act on oracle's recommendation.
-
-Don't fire oracle blind to "save a turn" — it will give worse advice.
-
-## Anti-patterns
-
-- One huge "explore everything about X" agent → slow, unfocused result
-- Serial explores ("wait for first, then fire next") → unnecessary latency
-- Firing 8+ parallel agents → diminishing returns, harder to synthesize
-- Calling `agent__collect` immediately after spawn → wastes a turn

assets/skills/verification-gates/SKILL.md

@@ -1,66 +0,0 @@
----
-description: Evidence requirements before claiming completion — diagnostics, build exit code, tests. No completion without proof. Grants shell access for running build/test commands.
-enabled_tools: execute_command
----
-You are about to mark work complete. Before claiming "done," produce evidence. "I'm fairly confident it works" is not evidence.
-
-## Hard gates
-
-A task is NOT complete until:
-
-| Change kind | Required evidence |
-|---|---|
-| File edit | Read the file to confirm the change landed; output is clean (or only pre-existing issues, explicitly noted) |
-| Build command exists | `execute_command` the build; exit code 0 |
-| Test command exists | `execute_command` the tests; pass (or explicit note of pre-existing failures unrelated to this change) |
-| Delegation | The delegate's result was received AND verified against your acceptance criteria |
-
-**No evidence = not complete.** Marking a todo done without evidence is dishonest reporting.
-
-## The verification loop
-
-After every meaningful edit:
-
-1. Read the changed file region (confirm the change actually landed where intended).
-2. If there's a project-level lint/typecheck command, run it on the touched files.
-3. Run the project's build/check command if one exists.
-4. Run the project's test command if one exists.
-5. Only then mark the corresponding todo `completed`.
-
-If any step fails: do not mark complete. Fix the issue or surface it explicitly.
-
-## Build/test detection (fallback)
-
-If no build/test command is configured, try standard ones for the project:
-
-- Rust: `cargo check`, `cargo test`
-- Node/TS: `npm run build`, `npm test`, or `pnpm` / `yarn` equivalents
-- Python: `pytest`, `python -m mypy <pkg>`, `ruff check`
-- Go: `go build ./...`, `go test ./...`
-
-Run from the project root. Capture exit codes.
-
-## Distinguishing your failures from pre-existing failures
-
-If build or tests fail, identify the cause:
-
-- Caused by your change? → fix it before reporting complete.
-- Pre-existing (unrelated)? → note it explicitly: "Done. Build passes. Note: 3 lint errors pre-existing in unrelated files, not touched."
-
-Never silently leave broken state behind. Never delete a failing test to make CI green.
-
-## Anti-patterns (BLOCKING)
-
-- "It should work" without running anything
-- Marking a todo complete based on intent, not verified outcome
-- Suppressing errors with `@ts-ignore`, `as any`, `#[allow(...)]` on unfamiliar lints, empty catch blocks
-- Deleting failing tests to "pass"
-- Reporting "all green" when you only ran a subset
-
-## Reporting completion
-
-When the work is verifiably done, report in one sentence:
-
-> "Done. Build passes, 47 tests pass. Modified `auth.rs:42-58` to add JWT validation."
-
-Not a paragraph. Not a victory lap. Specific, terse, evidence-backed.

config.agent.example.yaml

@@ -1,5 +1,5 @@
 # Agent-specific configuration
-# Location `<coyote-config-dir>/agents/<agent-name>/config.yaml`
+# Location `<loki-config-dir>/agents/<agent-name>/config.yaml`
 #
 # Available Environment Variables:
 #   - <agent-name>_MODEL
@@ -17,18 +17,16 @@ agent_session: null              # Set a session to use when starting the agent.
 name: <agent-name>               # Name of the agent, used in the UI and logs
 description: <description>       # Description of the agent, used in the UI
 version: 1                       # Version of the agent
-# Auto-Continue (Todo System)
+# Todo System & Auto-Continuation
-# The auto-continue system provides built-in task tracking for improved reliability.
+# These settings help smaller models handle multi-step tasks more reliably.
-# When enabled, the model can create todo lists and the system will automatically
+# See docs/TODO-SYSTEM.md for detailed documentation.
-# prompt it to continue when incomplete tasks remain.
-# See the [Todo System documentation](https://github.com/Dark-Alex-17/coyote/wiki/TODO-System) for more information
 auto_continue: false             # Enable automatic continuation when incomplete todos remain
 max_auto_continues: 10           # Maximum number of automatic continuations before stopping
 inject_todo_instructions: true   # Inject the default todo tool usage instructions into the agent's system prompt
 continuation_prompt: null        # Custom prompt used when auto-continuing (optional; uses default if null)
 # Sub-Agent Spawning System
 # Enable this agent to spawn and manage child agents in parallel.
-# See https://github.com/Dark-Alex-17/coyote/wiki/Agents for detailed documentation.
+# See docs/AGENTS.md for detailed documentation.
 can_spawn_agents: false          # Enable the agent to spawn child agents
 max_concurrent_agents: 4         # Maximum number of agents that can run simultaneously
 max_agent_depth: 3               # Maximum nesting depth for sub-agents (prevents runaway spawning)
@@ -37,23 +35,11 @@ summarization_model: null        # Model to use for summarizing sub-agent output
 summarization_threshold: 4000    # Character threshold above which sub-agent output is summarized before returning to parent
 escalation_timeout: 300          # Seconds a sub-agent waits for a user interaction response before timing out (default: 5 minutes)
 mcp_servers:                     # Optional list of MCP servers that the agent utilizes
-  - github                       # Corresponds to the name of an MCP server in the `<coyote-config-dir>/functions/mcp.json` file
+  - github                       # Corresponds to the name of an MCP server in the `<loki-config-dir>/functions/mcp.json` file
 global_tools:                    # Optional list of additional global tools to enable for the agent; i.e. not tools specific to the agent
   - web_search
   - fs
   - python
-skills_enabled: true             # Master switch for skills in this agent (default: inherit from global).
-                                 # Skills also require `function_calling_support: true` in the global config.
-enabled_skills:                  # Optional list of skills available when this agent runs.
-                                 # Must be a subset of global `visible_skills`. Omit to inherit the global default.
-  - git-master
-  - ai-slop-remover
-inject_skill_instructions: true  # Inject a short hint pointing the model at `skill__list` when skills are enabled
-                                 # (default: true). Suppressed automatically when no skills are available.
-skill_instructions: null         # Custom text for the skill hint (optional; uses built-in default if null)
-memory: null                     # Per-agent memory override (default: inherit). Set to `false` to disable memory
-                                 # for this agent regardless of workspace/global presence. See the Memory wiki page.
-
 dynamic_instructions: false      # Whether to use dynamic instructions for the agent; if false, static instructions are used
 instructions: |                  # Static instructions for the agent; ignored if dynamic instructions are used
   You are a AI agent designed to demonstrate agent capabilities.
@@ -92,10 +78,10 @@ conversation_starters:          # Optional conversation starters for the agent
   - What is the best way to exercise?
   - How do I manage my time effectively?
 documents:                     # Optional documents to load for the agent
-  - git:/some/repo             # Explicitly tell Coyote to use the 'git' document loader using an absolute path
+  - git:/some/repo             # Explicitly tell Loki to use the 'git' document loader using an absolute path
-  - pdf:some-pdf-file.pdf      # Explicitly tell Coyote to use the 'pdf' document loader using a relative path
+  - pdf:some-pdf-file.pdf      # Explicitly tell Loki to use the 'pdf' document loader using a relative path
   - https://some-website.com/some-page
-  - some-file.pdf              # File with relative path to the <coyote-config-dir>/agents/<agent-name> directory; i.e. file in the same directory as this config file
+  - some-file.pdf              # File with relative path to the <loki-config-dir>/agents/<agent-name> directory; i.e. file in the same directory as this config file
   - ~/some-file.txt            # File in the user's home directory
   - /absolute/path/to/some-file.md  # File with absolute path
   - /absolute/path/**/NAME.txt    # Find all NAME.txt files in the specified directory and all its subdirectories

config.example.yaml

@@ -18,78 +18,31 @@ agent_session: null              # Set a session to use when starting an agent (
 
 # ---- Appearance ----
 highlight: true                  # Controls syntax highlighting
-light_theme: false               # Activates a light color theme when true. env: COYOTE_LIGHT_THEME
+light_theme: false               # Activates a light color theme when true. env: LOKI_LIGHT_THEME
 
 # ---- Miscellaneous ----
-user_agent: null                 # Set User-Agent HTTP header, use `auto` for coyote/<current-version>
+user_agent: null                 # Set User-Agent HTTP header, use `auto` for loki/<current-version>
 save_shell_history: true         # Whether to save shell execution command to the history file
 sync_models_url: >               # URL to sync model changes from
-  https://raw.githubusercontent.com/Dark-Alex-17/coyote/refs/heads/main/models.yaml
+  https://raw.githubusercontent.com/Dark-Alex-17/loki/refs/heads/main/models.yaml
 
 # ---- REPL Prompt ----
-# Custom REPL left/right prompts; see the [REPL Prompt Documentation](https://github.com/Dark-Alex-17/coyote/wiki/REPL-Prompt) for more information
+# Custom REPL left/right prompts; see the [REPL Prompt Documentation](./docs/REPL-PROMPT.md) for more information
 left_prompt:
   '{color.red}{model}){color.green}{?session {?agent {agent}>}{session}{?role /}}{!session {?agent {agent}>}}{role}{?rag @{rag}}{color.cyan}{?session )}{!session >}{color.reset} '
 right_prompt:
   '{color.purple}{?session {?consume_tokens {consume_tokens}({consume_percent}%)}{!consume_tokens {consume_tokens}}}{color.reset}'
 
 # ---- Vault ----
-# See the [Vault documentation](https://github.com/Dark-Alex-17/coyote/wiki/Vault) for more information on the Coyote vault.
+# See the [Vault documentation](./docs/VAULT.md) for more information on the Loki vault
-#
+vault_password_file: null        # Path to a file containing the password for the Loki vault (cannot be a secret template)
-# The secrets_provider tells Coyote where to read and write secrets referenced via {{SECRET_NAME}} syntax.
-#
-# Shorthand: set vault_password_file to enable the local provider with that password file.
-vault_password_file: null        # Path to a file containing the password for the Coyote vault (cannot be a secret template)
-#
-# Explicit: set secrets_provider to one of the supported types below. When secrets_provider is set,
-# vault_password_file is ignored. Note: secrets_provider itself cannot use {{SECRET}} template syntax.
-# The vault must be initialized before any secrets can be resolved.
-#
-# Local (same as the shorthand above):
-# secrets_provider:
-#   type: local
-#   password_file: ~/.coyote_password
-#
-# AWS Secrets Manager (requires an authenticated AWS CLI; see `aws sso login` or `aws configure`):
-# secrets_provider:
-#   type: aws_secrets_manager
-#   aws_profile: default
-#   aws_region: us-east-1
-#
-# GCP Secret Manager (requires `gcloud auth application-default login`):
-# secrets_provider:
-#   type: gcp_secret_manager
-#   gcp_project_id: my-project-id
-#
-# Azure Key Vault (requires `az login`):
-# secrets_provider:
-#   type: azure_key_vault
-#   vault_name: my-vault-name
-#
-# gopass (requires the `gopass` CLI to be installed and initialized):
-# secrets_provider:
-#   type: gopass
-#   store: my-store              # Optional; omit to use the default store
-#
-# 1Password (requires the `op` CLI to be installed and signed in via `op signin`):
-# secrets_provider:
-#   type: one_password
-#   vault: Production            # Optional; omit to use the default vault
-#   account: my.1password.com    # Optional; omit to use the default account
 
 # ---- Function Calling ----
-# See the [Tools documentation](https://github.com/Dark-Alex-17/coyote/wiki/Tools) for more details
+# See the [Tools documentation](./docs/function-calling/TOOLS.md) for more details
-function_calling_support: true           # Enables or disables function calling (Globally).
+function_calling: true           # Enables or disables function calling (Globally).
 mapping_tools:                   # Alias for a tool or toolset
   fs: 'fs_cat,fs_ls,fs_mkdir,fs_rm,fs_write,fs_read,fs_glob,fs_grep'
-enabled_tools: null              # Which tools to enable by default.
+enabled_tools: null              # Which tools to enable by default. (e.g. 'fs,web_search_loki')
-                                 # Accepts either a YAML list or a comma-separated string. Use 'all' to enable everything.
-                                 # Example (list form):
-                                 #   enabled_tools:
-                                 #     - fs
-                                 #     - web_search_coyote
-                                 # Example (comma-separated form):
-                                 #   enabled_tools: fs,web_search_coyote
 visible_tools:                   # Which tools are visible to be compiled (and are thus able to be defined in 'enabled_tools')
 #  - demo_py.py
 #  - demo_sh.sh
@@ -111,64 +64,25 @@ visible_tools:                   # Which tools are visible to be compiled (and a
 #  - get_current_weather.py
 #  - get_current_weather.ts
   - get_current_weather.sh
+  - query_jira_issues.sh
 #  - search_arxiv.sh
 #  - search_wikipedia.sh
 #  - search_wolframalpha.sh
 #  - send_mail.sh
 #  - send_twilio.sh
-#  - web_search_coyote.sh
+#  - web_search_loki.sh
 #  - web_search_perplexity.sh
 #  - web_search_tavily.sh
 
 # ---- MCP Servers ----
-# See the [MCP Servers documentation](https://github.com/Dark-Alex-17/coyote/wiki/MCP-Servers) for more details
+# See the [MCP Servers documentation](./docs/MCP-SERVERS.md) for more details
 mcp_server_support: true         # Enables or disables MCP servers (globally).
 mapping_mcp_servers:             # Alias for an MCP server or set of servers
   git: github,gitmcp
-enabled_mcp_servers: null        # Which MCP servers to enable by default.
+enabled_mcp_servers: null        # Which MCP servers to enable by default (e.g. 'github,slack,ddg-search')
-                                 # Accepts either a YAML list or a comma-separated string. Use 'all' to enable everything.
-                                 # Example (list form):
-                                 #   enabled_mcp_servers:
-                                 #     - github
-                                 #     - slack
-                                 # Example (comma-separated form):
-                                 #   enabled_mcp_servers: github,slack,ddg-search
-
-# ---- Skills ----
-# Skills are modular knowledge or capability packs the LLM can load and unload mid-conversation.
-# See the [Skills documentation](https://github.com/Dark-Alex-17/coyote/wiki/Skills) for more details.
-skills_enabled: true              # Master switch. Set to false to hide all skill management tools from the model.
-                                  # Skills also require `function_calling_support: true` above to work at all.
-visible_skills:                   # The universe of skills allowed to be enabled in any context. Omit (null) for "all installed".
-  - ai-slop-remover
-  - code-review
-  - frontend-ui-ux
-  - git-master
-enabled_skills: null              # Which skills are available by default (no role/agent/session active). null = all visible.
-                                  # Accepts either a YAML list or a comma-separated string.
-                                  # Example (list form):
-                                  #   enabled_skills:
-                                  #     - git-master
-                                  #     - ai-slop-remover
-                                  # Example (comma-separated form):
-                                  #   enabled_skills: git-master,ai-slop-remover
-inject_skill_instructions: true   # Inject a short hint pointing the model at `skill__list` when skills are enabled in
-                                  # this context. Only injected if `function_calling_support`, `skills_enabled`, and the
-                                  # effective enabled skill set is non-empty (default: true).
-skill_instructions: null          # Custom text used for the skill hint when injected. If null, uses built-in default.
-
-# ---- Auto-Continue (Todo System) ----
-# The auto-continue system provides built-in task tracking for improved reliability.
-# When enabled, the model can create todo lists and the system will automatically
-# prompt it to continue when incomplete tasks remain.
-# See the [Todo System documentation](https://github.com/Dark-Alex-17/coyote/wiki/TODO-System) for more information
-auto_continue: false              # Enable automatic continuation when incomplete todos remain (default: false)
-max_auto_continues: 10            # Maximum number of automatic continuations before stopping (default: 10)
-inject_todo_instructions: true    # Inject default todo usage instructions into the system prompt (default: true)
-continuation_prompt: null         # Custom prompt used when auto-continuing. If null, uses built-in default
 
 # ---- Session ----
-# See the [Session documentation](https://github.com/Dark-Alex-17/coyote/wiki/Sessions) for more information
+# See the [Session documentation](./docs/SESSIONS.md) for more information
 save_session: null               # Controls the persistence of the session. If true, auto save; if false, don't auto-save save; if null, ask the user what to do
 compression_threshold: 4000      # Compress the session when the token count reaches or exceeds this threshold
 summarization_prompt: >          # The text prompt used for creating a concise summary of session message
@@ -176,23 +90,10 @@ summarization_prompt: >          # The text prompt used for creating a concise s
 summary_context_prompt: >        # The text prompt used for including the summary of the entire session as context to the model
   'This is a summary of the chat history as a recap: '
 
-# ---- Memory ----
-# See the [Memory documentation](https://github.com/Dark-Alex-17/coyote/wiki/Memory) for more information.
-# Memory is opt-in by workspace presence (a `COYOTE.md` or `.coyote/memory/MEMORY.md`)
-# and global presence (`<config_dir>/memory/MEMORY.md`). Set `memory: false` to disable
-# even when memory files exist. The cascade is: agent > session > role > app.
-# Bootstrap with `coyote --init-memory [global|workspace]` to create the marker file
-# the LLM needs before it will write any memory.
-memory: null                     # null = enabled when memory exists on disk; true = force on; false = force off
-memory_cap_with_tools: null      # Char cap for injected memory when function calling is available (default: 6000).
-                                 # Only MEMORY.md indexes are injected; the LLM uses memory__read to fetch drill files.
-memory_cap_without_tools: null   # Char cap when function calling is unavailable (default: 12000).
-                                 # Indexes plus drill file bodies are injected up to this cap.
-
 # ---- RAG ----
-# See the [RAG Docs](https://github.com/Dark-Alex-17/coyote/wiki/RAG) for more details.
+# See the [RAG Docs](./docs/RAG.md) for more details.
 rag_embedding_model: null        # Specifies the embedding model used for context retrieval
-rag_reranker_model: null         # Specifies the reranker model used for sorting retrieved documents; Coyote uses Reciprocal Rank Fusion by default
+rag_reranker_model: null         # Specifies the reranker model used for sorting retrieved documents; Loki uses Reciprocal Rank Fusion by default
 rag_top_k: 5                     # Specifies the number of documents to retrieve for answering queries
 rag_chunk_size: null             # Defines the size of chunks for document processing in characters
 rag_chunk_overlap: null          # Defines the overlap between chunks
@@ -231,12 +132,12 @@ document_loaders:
   docx: 'pandoc --to plain $1'                                                          # Use pandoc to convert a .docx file to text
   # (see https://pandoc.org for details on how to install pandoc)
   jina: 'curl -fsSL https://r.jina.ai/$1 -H "Authorization: Bearer {{JINA_API_KEY}}'    # Use Jina to translate a website into text;
-  # Requires a Jina API key to be added to the Coyote vault
+  # Requires a Jina API key to be added to the Loki vault
   git: >                                                                                # Use yek to load a git repository into the knowledgebase (https://github.com/bodo-run/yek)
     sh -c "yek $1 --json | jq 'map({ path: .filename, contents: .content })'"
 
 # ---- Clients ----
-# See the [Clients documentation](https://github.com/Dark-Alex-17/coyote/wiki/Clients) for more details
+# See the [Clients documentation](./docs/clients/CLIENTS.md) for more details
 clients:
   # All clients have the following configuration:
   # - type: xxxx
@@ -267,14 +168,14 @@ clients:
   # See https://platform.openai.com/docs/quickstart
   - type: openai
     api_base: https://api.openai.com/v1               # Optional
-    api_key: '{{OPENAI_API_KEY}}'                     # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{OPENAI_API_KEY}}'                     # You can either hard-code or inject secrets from the Loki vault
     organization_id: org-xxx                          # Optional
 
   # For any platform compatible with OpenAI's API
   - type: openai-compatible
     name: ollama
     api_base: http://localhost:11434/v1
-    api_key: '{{OLLAMA_API_KEY}}'                     # Optional; You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{OLLAMA_API_KEY}}'                     # Optional; You can either hard-code or inject secrets from the Loki vault
     models:
       - name: deepseek-r1
         max_input_tokens: 131072
@@ -292,9 +193,9 @@ clients:
   # See https://ai.google.dev/docs
   - type: gemini
     api_base: https://generativelanguage.googleapis.com/v1beta
-    api_key: '{{GEMINI_API_KEY}}'                       # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{GEMINI_API_KEY}}'                       # You can either hard-code or inject secrets from the Loki vault
-    auth: null                                          # When set to 'oauth', Coyote will use OAuth instead of an API key
+    auth: null                                          # When set to 'oauth', Loki will use OAuth instead of an API key
-                                                        # Authenticate with `coyote --authenticate` or `.authenticate` in the REPL
+                                                        # Authenticate with `loki --authenticate` or `.authenticate` in the REPL
     patch:
       chat_completions:
         '.*':
@@ -312,49 +213,49 @@ clients:
   # See https://docs.anthropic.com/claude/reference/getting-started-with-the-api
   - type: claude
     api_base: https://api.anthropic.com/v1              # Optional
-    api_key: '{{ANTHROPIC_API_KEY}}'                    # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{ANTHROPIC_API_KEY}}'                    # You can either hard-code or inject secrets from the Loki vault
-    auth: null                                          # When set to 'oauth', Coyote will use OAuth instead of an API key
+    auth: null                                          # When set to 'oauth', Loki will use OAuth instead of an API key
-                                                        # Authenticate with `coyote --authenticate` or `.authenticate` in the REPL
+                                                        # Authenticate with `loki --authenticate` or `.authenticate` in the REPL
 
   # See https://docs.mistral.ai/
   - type: openai-compatible
     name: mistral
     api_base: https://api.mistral.ai/v1
-    api_key: '{{MISTRAL_API_KEY}}'                      # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{MISTRAL_API_KEY}}'                      # You can either hard-code or inject secrets from the Loki vault
 
   # See https://docs.x.ai/docs
   - type: openai-compatible
     name: xai
     api_base: https://api.x.ai/v1
-    api_key: '{{XAI_API_KEY}}'                          # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{XAI_API_KEY}}'                          # You can either hard-code or inject secrets from the Loki vault
 
   # See https://docs.ai21.com/docs/overview
   - type: openai-compatible
     name: ai12
     api_base: https://api.ai21.com/studio/v1
-    api_key: '{{AI21_API_KEY}}'                         # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{AI21_API_KEY}}'                         # You can either hard-code or inject secrets from the Loki vault
 
   # See https://docs.cohere.com/docs/the-cohere-platform
   - type: cohere
     api_base: https://api.cohere.ai/v2                  # Optional
-    api_key: '{{COHERE_API_KEY}}'                       # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{COHERE_API_KEY}}'                       # You can either hard-code or inject secrets from the Loki vault
 
   # See https://docs.perplexity.ai/getting-started/overview
   - type: openai-compatible
     name: perplexity
     api_base: https://api.perplexity.ai
-    api_key: '{{PERPLEXITY_API_KEY}}'                   # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{PERPLEXITY_API_KEY}}'                   # You can either hard-code or inject secrets from the Loki vault
 
   # See https://console.groq.com/docs/quickstart
   - type: openai-compatible
     name: groq
     api_base: https://api.groq.com/openai/v1
-    api_key: '{{GROQ_API_KEY}}'                         # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{GROQ_API_KEY}}'                         # You can either hard-code or inject secrets from the Loki vault
 
   # See https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart
   - type: azure-openai
     api_base: https://{RESOURCE}.openai.azure.com
-    api_key: '{{AZURE_OPENAI_API_KEY}}'                 # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{AZURE_OPENAI_API_KEY}}'                 # You can either hard-code or inject secrets from the Loki vault
     models:
       - name: gpt-4o                                    # Model deployment name
         max_input_tokens: 128000
@@ -385,8 +286,8 @@ clients:
 
   # See https://docs.aws.amazon.com/bedrock/latest/userguide/
   - type: bedrock
-    access_key_id: '{{AWS_ACCESS_KEY_ID}}'              # You can either hard-code or inject secrets from the Coyote vault
+    access_key_id: '{{AWS_ACCESS_KEY_ID}}'              # You can either hard-code or inject secrets from the Loki vault
-    secret_access_key: '{{AWS_SECRET_ACCESS_KEY}}'      # You can either hard-code or inject secrets from the Coyote vault
+    secret_access_key: '{{AWS_SECRET_ACCESS_KEY}}'      # You can either hard-code or inject secrets from the Loki vault
     region: xxx
     session_token: xxx                                  # Optional, only needed for temporary credentials
 
@@ -394,67 +295,67 @@ clients:
   - type: openai-compatible
     name: cloudflare
     api_base: https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/v1
-    api_key: '{{CLOUDFLARE_API_KEY}}'                    # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{CLOUDFLARE_API_KEY}}'                    # You can either hard-code or inject secrets from the Loki vault
 
   # See https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html
   - type: openai-compatible
     name: ernie
     api_base: https://qianfan.baidubce.com/v2
-    api_key: '{{BAIDU_API_KEY}}'                        # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{BAIDU_API_KEY}}'                        # You can either hard-code or inject secrets from the Loki vault
 
   # See https://dashscope.aliyun.com/
   - type: openai-compatible
     name: qianwen
     api_base: https://dashscope.aliyuncs.com/compatible-mode/v1
-    api_key: '{{ALIYUN_API_KEY}}'                       # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{ALIYUN_API_KEY}}'                       # You can either hard-code or inject secrets from the Loki vault
 
   # See https://cloud.tencent.com/product/hunyuan
   - type: openai-compatible
     name: hunyuan
     api_base: https://api.hunyuan.cloud.tencent.com/v1
-    api_key: '{{TENCENT_API_KEY}}'                      # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{TENCENT_API_KEY}}'                      # You can either hard-code or inject secrets from the Loki vault
 
   # See https://platform.moonshot.cn/docs/intro
   - type: openai-compatible
     name: moonshot
     api_base: https://api.moonshot.cn/v1
-    api_key: '{{MOONSHOT_API_KEY}}'                     # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{MOONSHOT_API_KEY}}'                     # You can either hard-code or inject secrets from the Loki vault
 
   # See https://platform.deepseek.com/api-docs/
   - type: openai-compatible
     name: deepseek
     api_base: https://api.deepseek.com
-    api_key: '{{DEEPSEEK_API_KEY}}'                     # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{DEEPSEEK_API_KEY}}'                     # You can either hard-code or inject secrets from the Loki vault
 
   # See https://open.bigmodel.cn/dev/howuse/introduction
   - type: openai-compatible
     name: zhipuai
     api_base: https://open.bigmodel.cn/api/paas/v4
-    api_key: '{{ZHIPUAI_API_KEY}}'                      # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{ZHIPUAI_API_KEY}}'                      # You can either hard-code or inject secrets from the Loki vault
 
   # See https://platform.minimaxi.com/document/Fast%20access
   - type: openai-compatible
     name: minimax
     api_base: https://api.minimax.chat/v1
-    api_key: '{{MINIMAX_API_KEY}}'                      # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{MINIMAX_API_KEY}}'                      # You can either hard-code or inject secrets from the Loki vault
 
   # See https://openrouter.ai/docs#quick-start
   - type: openai-compatible
     name: openrouter
     api_base: https://openrouter.ai/api/v1
-    api_key: '{{OPENROUTER_API_KEY}}'                   # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{OPENROUTER_API_KEY}}'                   # You can either hard-code or inject secrets from the Loki vault
 
   # See https://github.com/marketplace/models
   - type: openai-compatible
     name: github
     api_base: https://models.inference.ai.azure.com
-    api_key: '{{GITHUB_API_KEY}}'                       # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{GITHUB_API_KEY}}'                       # You can either hard-code or inject secrets from the Loki vault
 
   # See https://deepinfra.com/docs
   - type: openai-compatible
     name: deepinfra
     api_base: https://api.deepinfra.com/v1/openai
-    api_key: '{{DEEPINFRA_API_KEY}}'                    # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{DEEPINFRA_API_KEY}}'                    # You can either hard-code or inject secrets from the Loki vault
 
 
   # ----- RAG dedicated -----
@@ -463,10 +364,10 @@ clients:
   - type: openai-compatible
     name: jina
     api_base: https://api.jina.ai/v1
-    api_key: '{{JINA_API_KEY}}'                         # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{JINA_API_KEY}}'                         # You can either hard-code or inject secrets from the Loki vault
 
   # See https://docs.voyageai.com/docs/introduction
   - type: openai-compatible
     name: voyageai
     api_base: https://api.voyageai.com/v1
-    api_key: '{{VOYAGEAI_API_KEY}}'                     # You can either hard-code or inject secrets from the Coyote vault
+    api_key: '{{VOYAGEAI_API_KEY}}'                     # You can either hard-code or inject secrets from the Loki vault

config.role.example.md

@@ -1,40 +1,12 @@
 ---
-############################################
+# Everything in this section is optional
-## Everything in this section is optional ##
-############################################
-
-# Role Configuration
 name: <role-name>                     # The name of the role
 model: openai:gpt-4o                  # The model to use for this role
 temperature: 0.2                      # The temperature to use for this role when querying the model
 top_p: 0                              # The top_p to use for this role when querying the model
-enabled_tools:                        # Tools to enable for this role. Accepts a YAML list (preferred)
+enabled_tools: fs_ls,fs_cat           # A comma-separated list of tools to enable for this role
-  - fs_ls                             # or a comma-separated string (e.g. `enabled_tools: fs_ls,fs_cat`).
+enabled_mcp_servers: github,gitmcp    # A comma-separated list of MCP servers to enable for this role
-  - fs_cat                            # Use `all` to enable every visible tool.
-enabled_mcp_servers:                  # MCP servers to enable for this role. Accepts a YAML list (preferred)
-  - github                            # or a comma-separated string (e.g. `enabled_mcp_servers: github,gitmcp`).
-  - gitmcp                            # Use `all` to enable every configured MCP server.
-skills_enabled: true                  # Master switch for skills in this role (default: inherit from global).
-                                      # Skills also require `function_calling_support: true` in the global config.
-enabled_skills:                       # Skills available when this role is active. Accepts a YAML list (preferred)
-  - git-master                        # or a comma-separated string (e.g. `enabled_skills: git-master,ai-slop-remover`).
-  - ai-slop-remover                   # Must be a subset of global `visible_skills`. Omit to inherit the global default.
-inject_skill_instructions: true       # Inject a short hint pointing the model at `skill__list` when skills are enabled 
-                                      # (default: true). Suppressed automatically when no skills are available.
-skill_instructions: null              # Custom text for the skill hint (optional; uses built-in default if null)
-memory: null                          # Per-role memory override (default: inherit). Set to `false` to disable memory
-                                      # when this role is active. See the Memory wiki page.
-
 prompt: null                          # A custom prompt to use for this role that will immediately query
                                       # the model for output instead of using the instructions below
-# Auto-Continue (Todo System)
-# The auto-continue system provides built-in task tracking for improved reliability.
-# When enabled, the model can create todo lists and the system will automatically
-# prompt it to continue when incomplete tasks remain.
-# See the [Todo System documentation](https://github.com/Dark-Alex-17/coyote/wiki/TODO-System) for more information
-auto_continue: false                  # Enable automatic continuation when incomplete todos remain (default: false)
-max_auto_continues: 10                # Maximum number of automatic continuations before stopping (default: 10)
-inject_todo_instructions: true        # Inject default todo tool usage instructions into the system prompt (default: true)
-continuation_prompt: null             # Custom prompt used when auto-continuing. If null, uses built-in default
 ---
 You are an expert at doing things. This is where you write the instructions for the role.

deployment/homebrew/coyote.rb.template

@@ -1,23 +0,0 @@
-# Documentation: https://docs.brew.sh/Formula-Cookbook
-#                https://rubydoc.brew.sh/Formula
-class Coyote < Formula
-  desc "All-in-one, batteries included LLM CLI tool"
-  homepage "https://github.com/Dark-Alex-17/coyote"
-  if OS.mac? and Hardware::CPU.arm?
-    url "https://github.com/Dark-Alex-17/coyote/releases/download/v$version/coyote-aarch64-apple-darwin.tar.gz"
-    sha256 "$hash_mac_arm"
-  elsif OS.mac? and Hardware::CPU.intel?
-    url "https://github.com/Dark-Alex-17/coyote/releases/download/v$version/coyote-x86_64-apple-darwin.tar.gz"
-    sha256 "$hash_mac"
-  else
-    url "https://github.com/Dark-Alex-17/coyote/releases/download/v$version/coyote-x86_64-unknown-linux-musl.tar.gz"
-    sha256 "$hash_linux"
-  end
-  version "$version"
-  license "MIT"
-
-  def install
-    bin.install "coyote"
-    ohai "You're done! Get started with \"coyote --help\""
-  end
-end

deployment/homebrew/loki.rb.template

@@ -0,0 +1,23 @@
+# Documentation: https://docs.brew.sh/Formula-Cookbook
+#                https://rubydoc.brew.sh/Formula
+class Loki < Formula
+  desc "All-in-one, batteries included LLM CLI tool"
+  homepage "https://github.com/Dark-Alex-17/loki"
+  if OS.mac? and Hardware::CPU.arm?
+    url "https://github.com/Dark-Alex-17/loki/releases/download/v$version/loki-aarch64-apple-darwin.tar.gz"
+    sha256 "$hash_mac_arm"
+  elsif OS.mac? and Hardware::CPU.intel?
+    url "https://github.com/Dark-Alex-17/loki/releases/download/v$version/loki-x86_64-apple-darwin.tar.gz"
+    sha256 "$hash_mac"
+  else
+    url "https://github.com/Dark-Alex-17/loki/releases/download/v$version/loki-x86_64-unknown-linux-musl.tar.gz"
+    sha256 "$hash_linux"
+  end
+  version "$version"
+  license "MIT"
+
+  def install
+    bin.install "loki"
+    ohai "You're done! Get started with \"loki --help\""
+  end
+end

docs/AGENTS.md

@@ -0,0 +1,775 @@
+# Agents
+
+Agents in Loki follow the same style as OpenAI's GPTs. They consist of 3 parts:
+
+* [Role](./ROLES.md) - Tell the LLM how to behave
+* [RAG](./RAG.md) - Pre-built knowledge bases specifically for the agent
+* [Function Calling](./function-calling/TOOLS.md#tools) ([#2](./function-calling/MCP-SERVERS.md)) - Extends the functionality of the LLM through custom functions it can call
+
+![Agent example](./images/agents/sql.gif)
+
+Agent configuration files are stored in the `agents` subdirectory of your Loki configuration directory. The location of
+this directory varies between systems so you can use the following command to locate yours:
+
+```shell
+loki --info | grep 'agents_dir' | awk '{print $2}'
+```
+
+If you're looking for more example agents, refer to the [built-in agents](../assets/agents).
+
+## Quick Links
+<!--toc:start-->
+- [Directory Structure](#directory-structure)
+- [Metadata](#1-metadata)
+- [2. Define the Instructions](#2-define-the-instructions)
+  - [Static Instructions](#static-instructions)
+    - [Special Variables](#special-variables)
+    - [User-Defined Variables](#user-defined-variables)
+  - [Dynamic Instructions](#dynamic-instructions)
+  - [Variables](#variables)
+- [3. Initializing RAG](#3-initializing-rag)
+- [4. Building Tools for Agents](#4-building-tools-for-agents)
+  - [Limitations](#limitations)
+  - [.env File Support](#env-file-support)
+  - [Python-Based Agent Tools](#python-based-agent-tools)
+  - [Bash-Based Agent Tools](#bash-based-agent-tools)
+  - [TypeScript-Based Agent Tools](#typescript-based-agent-tools)
+- [5. Conversation Starters](#5-conversation-starters)
+- [6. Todo System & Auto-Continuation](#6-todo-system--auto-continuation)
+- [7. Sub-Agent Spawning System](#7-sub-agent-spawning-system)
+  - [Configuration](#spawning-configuration)
+  - [Spawning & Collecting Agents](#spawning--collecting-agents)
+  - [Task Queue with Dependencies](#task-queue-with-dependencies)
+  - [Active Task Dispatch](#active-task-dispatch)
+  - [Output Summarization](#output-summarization)
+  - [Teammate Messaging](#teammate-messaging)
+  - [Runaway Safeguards](#runaway-safeguards)
+- [8. User Interaction Tools](#8-user-interaction-tools)
+  - [Available Tools](#user-interaction-available-tools)
+  - [Escalation (Sub-Agent to User)](#escalation-sub-agent-to-user)
+- [9. Auto-Injected Prompts](#9-auto-injected-prompts)
+- [Built-In Agents](#built-in-agents)
+<!--toc:end-->
+
+---
+
+## Directory Structure
+Agent configurations often have the following directory structure:
+
+```
+<loki-config-dir>/agents
+    └── my-agent
+        ├── config.yaml
+        ├── tools.sh
+            or
+        ├── tools.py
+            or
+        ├── tools.ts
+```
+
+This means that agent configurations often are only two files: the agent configuration file (`config.yaml`), and the 
+tool definitions (`agents/my-agent/tools.sh`, `tools.py`, or `tools.ts`).
+
+To see a full example configuration file, refer to the [example agent config file](../config.agent.example.yaml).
+
+The best way to understand how an agent is built is to go step by step in the following manner:
+
+---
+
+## 1. Metadata
+Agent configurations have the following settings available to customize each agent:
+
+```yaml
+# Model Configuration
+model: openai:gpt-4o                 # Specify the LLM to use
+temperature: null                    # Set default temperature parameter, range (0, 1)
+top_p: null                          # Set default top-p parameter, with a range of (0, 1) or (0, 2), depending on the model
+# Agent Metadata Configuration
+agent_session: null                  # Set a session to use when starting the agent. (e.g. temp, default); defaults to globally set agent_session
+# Agent Configuration
+name: <agent-name>                   # Name of the agent, used in the UI and logs
+description: <description>           # Description of the agent, used in the UI
+version: 1                           # Version of the agent
+# Function Calling Configuration
+mcp_servers:                         # Optional list of MCP servers that the agent utilizes
+  - github                           # Corresponds to the name of an MCP server in the `<loki-config-dir>/functions/mcp.json` file
+global_tools:                        # Optional list of additional global tools to enable for the agent; i.e. not tools specific to the agent
+  - web_search
+  - fs
+  - python
+# Todo System & Auto-Continuation (see "Todo System & Auto-Continuation" section below)
+auto_continue: false                 # Enable automatic continuation when incomplete todos remain
+max_auto_continues: 10               # Maximum continuation attempts before stopping
+inject_todo_instructions: true       # Inject todo tool instructions into system prompt
+continuation_prompt: null            # Custom prompt for continuations (optional)
+# Sub-Agent Spawning (see "Sub-Agent Spawning System" section below)
+can_spawn_agents: false              # Enable spawning child agents
+max_concurrent_agents: 4             # Max simultaneous child agents
+max_agent_depth: 3                   # Max nesting depth (prevents runaway)
+inject_spawn_instructions: true      # Inject spawning instructions into system prompt
+summarization_model: null            # Model for summarizing sub-agent output (e.g. 'openai:gpt-4o-mini')
+summarization_threshold: 4000        # Char count above which sub-agent output is summarized
+escalation_timeout: 300              # Seconds sub-agents wait for escalated user input (default: 5 min)
+```
+
+As mentioned previously: Agents utilize function calling to extend a model's capabilities. However, agents operate in 
+isolated environment, so in order for an agent to use a tool or MCP server that you have defined globally, you must 
+explicitly state which tools and/or MCP servers the agent uses. Otherwise, it is assumed that the agent doesn't use any 
+tools outside its own custom defined tools.
+
+And if you don't define a `agents/my-agent/tools.sh`, `agents/my-agent/tools.py`, or `agents/my-agent/tools.ts`, then the agent is really just a 
+`role`.
+
+You'll notice there are no settings for agent-specific tooling. This is because they are handled separately and 
+automatically. See the [Building Tools for Agents](#4-building-tools-for-agents) section below for more information.
+
+To see a full example configuration file, refer to the [example agent config file](../config.agent.example.yaml).
+
+## 2. Define the Instructions
+At their heart, agents function similarly to roles in that they tell the model how to behave. Agent configuration files
+have the following settings for the instruction definitions:
+
+```yaml
+dynamic_instructions:     # Whether to use dynamically generated instructions for the agent; if false, static instructions are used. False by default.
+instructions:             # Static instructions for the LLM; These are ignored if dynamic instructions are used
+variables:                # An array of optional variables that the agent expects and uses
+```
+
+### Static Instructions
+By default, Loki agents use statically defined instructions. Think of them as being identical to the instructions for a
+[role](./ROLES.md#instructions), because they virtually are. 
+
+**Example:**
+```yaml
+instructions: |
+  You are an AI agent designed to demonstrate agentic capabilities
+```
+
+Just like roles, agents support variable interpolation at runtime. There's two types of variables that can be 
+interpolated into the instructions at runtime: special variables (like roles have), and user-defined variables. Just 
+like roles, variables are interpolated into your instructions anywhere Loki sees the `{{variable}}` syntax.
+
+#### Special Variables
+The following special variables are provided by Loki at runtime and can be injected into your agent's instructions:
+
+| Name            | Description                                                         | Example                    |
+|-----------------|---------------------------------------------------------------------|----------------------------|
+| `__os__`        | Operating system name                                               | `linux`                    |
+| `__os_family__` | Operating system family                                             | `unix`                     |
+| `__arch__`      | System architecture                                                 | `x86_64`                   |
+| `__shell__`     | The current user's default shell                                    | `bash`                     |
+| `__locale__`    | The current user's preferred language and region settings           | `en-US`                    |
+| `__now__`       | Current timestamp in ISO 8601 format                                | `2025-11-07T10:15:44.268Z` |
+| `__cwd__`       | The current working directory                                       | `/tmp`                     |
+| `__tools__`     | A list of the enabled tools (global + mcp servers + agent-specific) |                            |
+
+#### User-Defined Variables
+Agents also support user-defined variables that can be interpolated into the instructions, and are made available to any
+agent-specific tools you define (see [Building Tools for Agents](#4-building-tools-for-agents) for more details on how to 
+create agent-specific tooling).
+
+The `variables` setting in an agent's config has the following fields:
+
+| Field         | Required | Description                                                                                        |
+|---------------|----------|----------------------------------------------------------------------------------------------------|
+| `name`        | *        | The name of the variable                                                                           |
+| `description` | *        | The description of the field                                                                       |
+| `default`     |          | A default value for the field. If left undefined, the user will be prompted for a value at runtime |
+
+These variables can be referenced in both the agent's instructions, and in the tool definitions via `LLM_AGENT_VAR_<name>`.
+
+**Example:**
+```yaml
+instructions: |
+  You are an agent who answers questions about a user's system.
+
+  <tools>
+  {{__tools__}}
+  </tools>
+
+  <system>
+  os: {{__os__}}
+  os_family: {{__os_family__}}
+  arch: {{__arch__}}
+  shell: {{__shell__}}
+  locale: {{__locale__}}
+  now: {{__now__}}
+  cwd: {{__cwd__}}
+  </system>
+
+  <user>
+  username: {{username}}
+  </user>
+variables:
+  - name: username                 # Accessible from the tool definitions via the `LLM_AGENT_VAR_USERNAME` environment variable
+    description: Your user name
+```
+
+### Dynamic Instructions
+Sometimes you may find it useful to dynamically generate instructions on startup. Whether that be via a call to Loki
+itself to generate them, or by some other means. Loki supports this type of behavior using a special function defined
+in your `agents/my-agent/tools.py`, `agents/my-agent/tools.sh`, or `agents/my-agent/tools.ts`.
+
+**Example: Instructions for a JSON-reader agent that specializes on each JSON input it receives**
+`agents/json-reader/tools.py`:
+```python
+import json
+from pathlib import Path
+from genson import SchemaBuilder
+
+def _instructions():
+    """Generates instructions for the agent dynamically"""
+    value = input("Enter a JSON file path OR paste raw JSON: ").strip()
+    if not value:
+        raise SystemExit("A file path or JSON string is required.")
+
+    p = Path(value)
+    if p.exists() and p.is_file():
+        json_file_path = str(p.resolve())
+        json_text = p.read_text(encoding="utf-8")
+    else:
+        try:
+            json.loads(value)
+        except json.JSONDecodeError as e:
+            raise SystemExit(f"Input is neither a file nor valid JSON.\n{e}")
+        json_file_path = "<provided-inline-json>"
+        json_text = value
+
+    try:
+        data = json.loads(json_text)
+    except json.JSONDecodeError as e:
+        raise SystemExit(f"Provided content is not valid JSON.\n{e}")
+
+    builder = SchemaBuilder()
+    builder.add_object(data)
+    json_schema = builder.to_schema()
+    return f"""
+        You are an AI agent that can view and filter JSON data with jq.
+        
+        ## Context
+        json_file_path: {json_file_path}
+        json_schema: {json.dumps(json_schema, indent=2)}
+    """
+```
+
+or
+
+`agents/json-reader/tools.sh`:
+```bash
+#!/usr/bin/env bash
+set -e
+
+# @meta require-tools jq,genson
+# @env LLM_OUTPUT=/dev/stdout The output path
+
+# @cmd Generates instructions for the agent dynamically
+_instructions() {
+	read -r -p "Enter a JSON file path OR paste raw JSON: " value
+	
+	if [[ -z "${value}" ]]; then
+		echo "A file path or JSON string is required" >&2
+		exit 1
+	fi 
+	json_file_path=""
+    inline_temp=""
+    cleanup() {
+      [[ -n "${inline_temp:-}" && -f "${inline_temp}" ]] && rm -f "${inline_temp}"
+    }
+    trap cleanup EXIT
+    
+    if [[ -f "${value}" ]]; then
+      json_file_path="$(realpath "${value}")"
+      if ! jq empty "${json_file_path}" >/dev/null 2>&1; then
+        echo "Error: File does not contain valid JSON: ${json_file_path}" >&2
+        exit 1
+      fi
+    else
+      inline_temp="$(mktemp)"
+      printf "%s" "${value}" > "${inline_temp}"
+      if ! jq empty "${inline_temp}" >/dev/null 2>&1; then
+        echo "Error: Input is neither a file nor valid JSON." >&2
+        exit 1
+      fi
+      json_file_path="<provided-inline-json>"
+    fi
+    
+    source_file="${json_file_path}"
+    if [[ "${json_file_path}" == "<provided-inline-json>" ]]; then
+      source_file="${inline_temp}"
+    fi
+    
+    json_schema="$(genson < "${source_file}" | jq -c '.')"
+	cat <<EOF >> "$LLM_OUTPUT"
+You are an AI agent that can view and filter JSON data with jq.
+
+## Context
+json_file_path: ${json_file_path}
+json_schema: ${json_schema}
+EOF
+}
+```
+
+For more information on how to create custom tools for your agent and the structure of the `agent/my-agent/tools.sh`,
+`agent/my-agent/tools.py`, or `agent/my-agent/tools.ts` files, refer to the [Building Tools for Agents](#4-building-tools-for-agents) section below.
+
+#### Variables
+All the same variable interpolations supported by static instructions is also supported by dynamic instructions. For 
+more information on what variables are available and how to use them, refer to the [Special Variables](#special-variables)
+and [User-Defined Variables](#user-defined-variables) sections above.
+
+## 3. Initializing RAG
+Each agent you create also has a dedicated knowledge base that adds additional context to your queries and helps the LLM
+answer queries effectively. The documents to load into RAG are defined in the `documents` array of your agent 
+configuration file:
+
+```yaml
+documents:
+  - https://www.ohdsi.org/data-standardization/
+  - https://github.com/OHDSI/Vocabulary-v5.0/wiki/**
+  - OMOPCDM_ddl.sql       # Relative path to agent (i.e. file lives at '<loki-config-dir>/agents/my-agent/OMOPCDM_ddl.sql')
+```
+
+These documents use the same syntax as those you'd define when constructing RAG normally. To see all the available types
+of documents that Loki supports and how to use custom document loaders, refer to the [RAG documentation](./RAG.md#supported-document-sources).
+
+Anytime your agent starts up, it will automatically be using the RAG you've defined here.
+
+## 4. Building Tools for Agents
+Building tools for agents is virtually identical to building custom tools, with one slight difference: instead of 
+defining a single function that gets executed at runtime (e.g. `main` for bash tools and `run` for Python tools), agent
+tools define a number of *subcommands*.
+
+### Limitations
+You can only utilize one of: a bash-based `<loki-config-dir>/agents/my-agent/tools.sh`, a Python-based 
+`<loki-config-dir>/agents/my-agent/tools.py`, or a TypeScript-based `<loki-config-dir>/agents/my-agent/tools.ts`. 
+However, if it's easier to achieve a task in one language vs the other, 
+you're free to define other scripts in your agent's configuration directory and reference them from the main 
+tools file. **Any scripts *not* named `tools.{py,sh,ts}` will not be picked up by Loki's compiler**, meaning they 
+can be used like any other set of scripts.
+
+It's important to keep in mind the following:
+
+* **Do not give agents the same name as an executable**. Loki compiles the tools for each agent into a binary that it
+  temporarily places on your path during execution. If you have a binary with the same name as your agent, then your 
+  shell may execute the existing binary instead of your agent's tools
+* **`LLM_ROOT_DIR` points to the agent's configuration directory**. This is where agents differ slightly from normal 
+  tools: The `LLM_ROOT_DIR` environment variable does *not* point to the `functions/tools` directory like it does in 
+  global tools. Instead, it points to the agent's configuration directory, making it easier to source scripts and other
+  miscellaneous files
+
+### .env File Support
+When Loki loads an agent, it will also search the agent's configuration directory for a `.env` file. If found, all 
+environment variables defined in the file will be made available to the agent's tools.
+
+### Python-Based Agent Tools
+Python-based tools are defined exactly the same as they are for custom tool definitions. The only difference is that 
+instead of a single `run` function, you define as many as you like with whatever arguments you like.
+
+**Example:**
+`agents/my-agent/tools.py`
+```python
+import urllib.request
+
+def get_ip_info():
+  """
+  Get your IP information
+  """
+  with urllib.request.urlopen("https://httpbin.org/ip") as response:
+    data = response.read()
+    return data.decode('utf-8')
+
+def get_ip_address_from_aws():
+    """
+    Find your public IP address using AWS
+    """
+    with urllib.request.urlopen("https://checkip.amazonaws.com") as response:
+        data = response.read()
+        return data.decode('utf-8')
+```
+
+Loki automatically compiles these as separate functions for the LLM to call. No extra work is needed. Just make sure you
+follow all the same steps to define each function as you would when creating custom Python tools.
+
+For more information on how to build tools in Python, refer to the [custom Python tools documentation](./function-calling/CUSTOM-TOOLS.md#custom-python-based-tools)
+
+### Bash-Based Agent Tools
+Bash-based agent tools are virtually identical to custom bash tools, with only one difference. Instead of defining a 
+single entrypoint via the `main` function, you actually define as many subcommands as you like.
+
+**Example:**
+`agents/my-agent/tools.sh`
+```bash
+#!/usr/bin/env bash
+
+# @env LLM_OUTPUT=/dev/stdout The output path
+# @describe Discover network information about your computer and its place in the internet
+
+# Use the `@cmd` annotation to define subcommands for your script.
+# @cmd Get your IP information
+get_ip_info() {
+  curl -fsSL https://httpbin.org/ip >> "$LLM_OUTPUT"
+}
+
+# @cmd Find your public IP address using AWS
+get_ip_address_from_aws() {
+  curl -fsSL https://checkip.amazonaws.com >> "$LLM_OUTPUT"
+}
+```
+To compile the script so it's executable and testable:
+```bash
+$ loki --build-tools
+```
+
+Then you can execute your script (assuming your current working directory is `agents/my-agent`):
+```bash
+$ ./tools.sh get_ip_info
+$ ./tools.sh get_ip_address_from_aws
+```
+
+All other special annotations (`@env`, `@arg`, `@option` `@flags`) apply to subcommands as well, so be sure to follow 
+the same syntax ad formatting as is used to create custom bash tools globally.
+
+For more information on how to write, [build and test](function-calling/CUSTOM-BASH-TOOLS.md#execute-and-test-your-bash-tools) tools in bash, refer to the 
+[custom bash tools documentation](function-calling/CUSTOM-BASH-TOOLS.md).
+
+### TypeScript-Based Agent Tools
+TypeScript-based agent tools work exactly the same as TypeScript global tools. Instead of a single `run` function,
+you define as many exported functions as you like. Non-exported functions are private helpers and are invisible to the 
+LLM.
+
+**Example:**
+`agents/my-agent/tools.ts`
+```typescript
+/**
+ * Get your IP information
+ */
+export async function get_ip_info(): Promise<string> {
+  const resp = await fetch("https://httpbin.org/ip");
+  return await resp.text();
+}
+
+/**
+ * Find your public IP address using AWS
+ */
+export async function get_ip_address_from_aws(): Promise<string> {
+  const resp = await fetch("https://checkip.amazonaws.com");
+  return await resp.text();
+}
+
+// Non-exported helper — invisible to the LLM
+function formatResponse(data: string): string {
+  return data.trim();
+}
+```
+
+Loki automatically compiles each exported function as a separate tool for the LLM to call. Just make sure you
+follow the same JSDoc and parameter conventions as you would when creating custom TypeScript tools.
+
+TypeScript agent tools also support dynamic instructions via an exported `_instructions()` function:
+
+```typescript
+import { readFileSync } from "fs";
+
+/**
+ * Generates instructions for the agent dynamically
+ */
+export function _instructions(): string {
+  const schema = readFileSync("schema.json", "utf-8");
+  return `You are an AI agent that works with the following schema:\n${schema}`;
+}
+```
+
+For more information on how to build tools in TypeScript, refer to the [custom TypeScript tools documentation](function-calling/CUSTOM-TOOLS.md#custom-typescript-based-tools).
+
+## 5. Conversation Starters
+It's often helpful to also have some conversation starters so users know what kinds of things the agent is capable of 
+doing. These are available in the REPL via the `.starter` command and are selectable.
+
+They are defined using the `conversation_starters` setting in your agent's configuration file:
+
+**Example:**
+`agents/my-agent/config.yaml`:
+```yaml
+conversation_starters:
+  - What is my username?
+  - What is my current shell?
+  - What is my ip?
+  - How much disk space is left on my PC??
+  - How to create an agent?
+```
+
+![Example Conversation Starters](./images/agents/conversation-starters.gif)
+
+## 6. Todo System & Auto-Continuation
+
+Loki includes a built-in task tracking system designed to improve the reliability of agents, especially when using
+smaller language models. The Todo System helps models:
+
+- Break complex tasks into manageable steps
+- Track progress through multi-step workflows
+- Automatically continue work until all tasks are complete
+
+### Quick Configuration
+
+```yaml
+# agents/my-agent/config.yaml
+auto_continue: true              # Enable auto-continuation
+max_auto_continues: 10           # Max continuation attempts
+inject_todo_instructions: true   # Include the default todo instructions into prompt
+```
+
+### How It Works
+
+1. When `inject_todo_instructions` is enabled, agents receive instructions on using five built-in tools:
+    - `todo__init`: Initialize a todo list with a goal
+    - `todo__add`: Add a task to the list
+    - `todo__done`: Mark a task complete
+    - `todo__list`: View current todo state
+    - `todo__clear`: Clear the entire todo list and reset the goal
+   
+   These instructions are a reasonable default that detail how to use Loki's To-Do System. If you wish, 
+   you can disable the injection of the default instructions and specify your own instructions for how 
+   to use the To-Do System into your main `instructions` for the agent.
+
+2. When `auto_continue` is enabled and the model stops with incomplete tasks, Loki automatically sends a
+   continuation prompt with the current todo state, nudging the model to continue working.
+
+3. This continues until all tasks are done or `max_auto_continues` is reached.
+
+### When to Use
+
+- Multistep tasks where the model might lose track
+- Smaller models that need more structure
+- Workflows requiring guaranteed completion of all steps
+
+For complete documentation including all configuration options, tool details, and best practices, see the
+[Todo System Guide](./TODO-SYSTEM.md).
+
+## 7. Sub-Agent Spawning System
+
+Loki agents can spawn and manage child agents that run **in parallel** as background tasks inside the same process.
+This enables orchestrator-style agents that delegate specialized work to other agents, similar to how tools like
+Claude Code or OpenCode handle complex multi-step tasks.
+
+For a working example of an orchestrator agent that uses sub-agent spawning, see the built-in
+[sisyphus](../assets/agents/sisyphus) agent. For an example of the teammate messaging pattern with parallel sub-agents, 
+see the [code-reviewer](../assets/agents/code-reviewer) agent.
+
+### Spawning Configuration
+
+| Setting                     | Type    | Default       | Description                                                                    |
+|-----------------------------|---------|---------------|--------------------------------------------------------------------------------|
+| `can_spawn_agents`          | boolean | `false`       | Enable this agent to spawn child agents                                        |
+| `max_concurrent_agents`     | integer | `4`           | Maximum number of child agents that can run simultaneously                     |
+| `max_agent_depth`           | integer | `3`           | Maximum nesting depth for sub-agents (prevents runaway spawning chains)        |
+| `inject_spawn_instructions` | boolean | `true`        | Inject the default spawning instructions into the agent's system prompt        |
+| `summarization_model`       | string  | current model | Model to use for summarizing long sub-agent output (e.g. `openai:gpt-4o-mini`) |
+| `summarization_threshold`   | integer | `4000`        | Character count above which sub-agent output is summarized before returning    |
+| `escalation_timeout`        | integer | `300`         | Seconds a sub-agent waits for an escalated user interaction response           |
+
+**Example configuration:**
+```yaml
+# agents/my-orchestrator/config.yaml
+can_spawn_agents: true
+max_concurrent_agents: 6
+max_agent_depth: 2
+inject_spawn_instructions: true
+summarization_model: openai:gpt-4o-mini
+summarization_threshold: 3000
+escalation_timeout: 600
+```
+
+### Spawning & Collecting Agents
+
+When `can_spawn_agents` is enabled, the agent receives tools for spawning and managing child agents:
+
+| Tool             | Description                                                             |
+|------------------|-------------------------------------------------------------------------|
+| `agent__spawn`   | Spawn a child agent in the background. Returns an agent ID immediately. |
+| `agent__check`   | Non-blocking check: is the agent done? Returns `PENDING` or the result. |
+| `agent__collect` | Blocking wait: wait for an agent to finish, return its output.          |
+| `agent__list`    | List all spawned agents and their status.                               |
+| `agent__cancel`  | Cancel a running agent by ID.                                           |
+
+The core pattern is **Spawn -> Continue -> Collect**:
+
+```
+# 1. Spawn agents in parallel (returns IDs immediately)
+agent__spawn --agent explore --prompt "Find auth middleware patterns in src/"
+agent__spawn --agent explore --prompt "Find error handling patterns in src/"
+
+# 2. Continue your own work while they run
+
+# 3. Check if done (non-blocking)
+agent__check --id agent_explore_a1b2c3d4
+
+# 4. Collect results when ready (blocking)
+agent__collect --id agent_explore_a1b2c3d4
+agent__collect --id agent_explore_e5f6g7h8
+```
+
+Any agent defined in your `<loki-config-dir>/agents/` directory can be spawned as a child. Child agents:
+- Run in a fully isolated environment (separate session, config, and tools)
+- Have their output suppressed from the terminal (no spinner, no tool call logging)
+- Return their accumulated output to the parent when collected
+
+### Task Queue with Dependencies
+
+For complex workflows where tasks have ordering requirements, the spawning system includes a dependency-aware
+task queue:
+
+| Tool                   | Description                                                                 |
+|------------------------|-----------------------------------------------------------------------------|
+| `agent__task_create`   | Create a task with optional dependencies and auto-dispatch agent.           |
+| `agent__task_list`     | List all tasks with their status, dependencies, and assignments.            |
+| `agent__task_complete` | Mark a task done. Returns newly unblocked tasks and auto-dispatches agents. |
+| `agent__task_fail`     | Mark a task as failed. Dependents remain blocked.                           |
+
+```
+# Create tasks with dependency ordering
+agent__task_create --subject "Explore existing patterns"
+agent__task_create --subject "Implement feature" --blocked_by ["task_1"]
+agent__task_create --subject "Write tests" --blocked_by ["task_2"]
+
+# Mark tasks complete to unblock dependents
+agent__task_complete --task_id task_1
+```
+
+### Active Task Dispatch
+
+Tasks can optionally specify an agent to auto-spawn when the task becomes runnable:
+
+```
+agent__task_create \
+  --subject "Implement the auth module" \
+  --blocked_by ["task_1"] \
+  --agent coder \
+  --prompt "Implement auth module based on patterns found in task_1"
+```
+
+When `task_1` completes and the dependent task becomes unblocked, an agent is automatically spawned with the
+specified prompt. No manual intervention needed. This enables fully automated multi-step pipelines.
+
+### Output Summarization
+
+When a child agent produces long output, it can be automatically summarized before returning to the parent.
+This keeps parent context windows manageable.
+
+- If the output exceeds `summarization_threshold` characters (default: 4000), it is sent through an LLM
+  summarization pass
+- The `summarization_model` setting lets you use a cheaper/faster model for summarization (e.g. `gpt-4o-mini`)
+- If `summarization_model` is not set, the parent's current model is used
+- The summarization preserves all actionable information: code snippets, file paths, error messages, and
+  concrete recommendations
+
+### Teammate Messaging
+
+All agents (including children) automatically receive tools for **direct sibling-to-sibling messaging**:
+
+| Tool                  | Description                                         |
+|-----------------------|-----------------------------------------------------|
+| `agent__send_message` | Send a text message to another agent's inbox by ID. |
+| `agent__check_inbox`  | Drain all pending messages from your inbox.         |
+
+This enables coordination patterns where child agents share cross-cutting findings:
+
+```
+# Agent A discovers something relevant to Agent B
+agent__send_message --id agent_reviewer_b1c2d3e4 --message "Found a security issue in auth.rs line 42"
+
+# Agent B checks inbox before finalizing
+agent__check_inbox
+```
+
+Messages are routed through the parent's supervisor. A parent can message its children, and children can message
+their siblings. For a working example of the teammate pattern, see the built-in
+[code-reviewer](../assets/agents/code-reviewer) agent, which spawns file-specific reviewers that share
+cross-cutting findings with each other.
+
+### Runaway Safeguards
+
+The spawning system includes built-in safeguards to prevent runaway agent chains:
+
+- **`max_concurrent_agents`:** Caps how many agents can run at once (default: 4). Spawn attempts beyond this
+  limit return an error asking the agent to wait or cancel existing agents.
+- **`max_agent_depth`:** Caps nesting depth (default: 3). A child agent spawning its own child increments the
+  depth counter. Attempts beyond the limit are rejected.
+- **`can_spawn_agents`:** Only agents with this flag set to `true` can spawn children. By default, spawning is
+  disabled. This means child agents cannot spawn their own children unless you explicitly create them with
+  `can_spawn_agents: true` in their config.
+
+## 8. User Interaction Tools
+
+Loki includes built-in tools for agents (and the REPL) to interactively prompt the user for input. These tools
+are **always available**. No configuration needed. They are automatically injected into every agent and into
+REPL mode when function calling is enabled.
+
+### User Interaction Available Tools
+
+| Tool             | Description                             | Returns                          |
+|------------------|-----------------------------------------|----------------------------------|
+| `user__ask`      | Present a single-select list of options | The selected option string       |
+| `user__confirm`  | Ask a yes/no question                   | `"yes"` or `"no"`                |
+| `user__input`    | Request free-form text input            | The text entered by the user     |
+| `user__checkbox` | Present a multi-select checkbox list    | Array of selected option strings |
+
+**Parameters:**
+
+- `user__ask`: `--question "..." --options ["Option A", "Option B", "Option C"]`
+- `user__confirm`: `--question "..."`
+- `user__input`: `--question "..."`
+- `user__checkbox`: `--question "..." --options ["Option A", "Option B", "Option C"]`
+
+At the top level (depth 0), these tools render interactive terminal prompts directly using arrow-key navigation,
+checkboxes, and text input fields.
+
+### Escalation (Sub-Agent to User)
+
+When a **child agent** (depth > 0) calls a `user__*` tool, it cannot prompt the terminal directly. Instead,
+the request is **automatically escalated** to the root agent:
+
+1. The child agent calls `user__ask(...)` and **blocks**, waiting for a reply
+2. The root agent sees a `pending_escalations` notification in its next tool results
+3. The root agent either answers from context or prompts the user itself, then calls
+   `agent__reply_escalation` to unblock the child
+4. The child receives the reply and continues
+
+The escalation timeout is configurable via `escalation_timeout` in the agent's `config.yaml` (default: 300
+seconds / 5 minutes). If the timeout expires, the child receives a fallback message asking it to use its
+best judgment.
+
+| Tool                      | Description                                                              |
+|---------------------------|--------------------------------------------------------------------------|
+| `agent__reply_escalation` | Reply to a pending child escalation, unblocking the waiting child agent. |
+
+This tool is automatically available to any agent with `can_spawn_agents: true`.
+
+## 9. Auto-Injected Prompts
+
+Loki automatically appends usage instructions to your agent's system prompt for each enabled built-in system.
+These instructions are injected into both **static and dynamic instructions** after your own instructions,
+ensuring agents always know how to use their available tools.
+
+| System             | Injected When                                                  | Toggle                      |
+|--------------------|----------------------------------------------------------------|-----------------------------|
+| Todo tools         | `auto_continue: true` AND `inject_todo_instructions: true`     | `inject_todo_instructions`  |
+| Spawning tools     | `can_spawn_agents: true` AND `inject_spawn_instructions: true` | `inject_spawn_instructions` |
+| Teammate messaging | Always (all agents)                                            | None (always injected)      |
+| User interaction   | Always (all agents)                                            | None (always injected)      |
+
+If you prefer to write your own instructions for a system, set the corresponding `inject_*` flag to `false`
+and include your custom instructions in the agent's `instructions` field. The built-in tools will still be
+available; only the auto-injected prompt text is suppressed.
+
+## Built-In Agents
+Loki comes packaged with some useful built-in agents:
+
+* `coder`: An agent to assist you with all your coding tasks
+* `code-reviewer`: A [CodeRabbit](https://coderabbit.ai)-style code reviewer that spawns per-file reviewers using the teammate messaging pattern
+* `demo`: An example agent to use for reference when learning to create your own agents
+* `explore`: An agent designed to help you explore and understand your codebase
+* `file-reviewer`: An agent designed to perform code-review on a single file (used by the `code-reviewer` agent)
+* `jira-helper`: An agent that assists you with all your Jira-related tasks
+* `oracle`: An agent for high-level architecture, design decisions, and complex debugging
+* `sisyphus`: A powerhouse orchestrator agent for writing complex code and acting as a natural language interface for your codebase (similar to ClaudeCode, Gemini CLI, Codex, or OpenCode). Uses sub-agent spawning to delegate to `explore`, `coder`, and `oracle`.
+* `sql`: A universal SQL agent that enables you to talk to any relational database in natural language

docs/AICHAT-MIGRATION.md

@@ -0,0 +1,211 @@
+# AIChat to Loki Migration Guide
+Loki originally started as a fork of AIChat but has since evolved into its own separate project with separate goals.
+
+As a result, there's some changes you'll need to make to your AIChat configuration to be able to use Loki.
+
+Be sure you've run `loki` at least once so that the Loki configuration directory and subdirectories exist and is
+populated with the built-in defaults.
+
+## Global Configuration File
+You should be able to copy/paste your AIChat configuration file into your Loki configuration directory. Since the 
+location of the Loki configuration directory varies between systems, you can use the following command to locate your
+config directory:
+
+```shell
+loki --info | grep 'config_dir' | awk '{print $2}'
+```
+
+Then, you'll need to make the following changes:
+
+* `function_calling` -> `function_calling_support`
+* `use_tools` -> `enabled_tools`
+* `agent_prelude` -> `agent_session`
+* `compress_threshold` -> `compression_threshold`
+* `summarize_prompt` -> `summarization_prompt`
+* `summary_prompt` -> `summary_context_prompt`
+
+## Roles
+Locate your `roles` directory using the following command:
+
+```shell
+loki --info | grep 'roles_dir' | awk '{print $2}'
+```
+
+Update any roles that have `use_tools` to `enabled_tools`.
+
+## Sessions
+Locate your `sessions` directory using the following command:
+
+```shell
+loki --info | grep 'sessions_dir' | awk '{print $2}'
+```
+
+Update the following settings:
+* `use_tools` -> `enabled_tools`
+* `compress_threshold` -> `compression_threshold`
+* `summarize_prompt` -> `summarization_prompt`
+* `summary_prompt` -> `summary_context_prompt`
+
+---
+
+# LLM Functions Changes
+Probably the most significant difference between AIChat and Loki is how tools are handled. So if you cloned the 
+[llm-functions](https://github.com/sigoden/llm-functions) repo, you'll need to make the following changes.
+
+**Note: JavaScript functions are not supported in Loki.**
+
+The following guide assumes you're using the `llm-functions` repository as your base for custom functions, and thus
+follows that directory structure.
+
+## Agents
+Agents are now all handled in one place: the `agents` directory (`<loki-config-dir>/agents`):
+
+```shell
+loki --info | grep 'agents_dir' | awk '{print $2}'
+```
+
+And instead of separate `index.yaml` and `config.yaml` files, they're now both in a single `config.yaml` file.
+
+So now for all of your agents, copy all the contents of those directories to the corresponding directory in the Loki 
+`agents` directory. Then make the following changes:
+
+* Copy the contents of your `<aichat-config-dir>/functions/agents` directory into `<loki-config-dir/agents`
+* Merge `index.yaml` into `config.yaml`
+  * If you never created a custom `config.yaml` file, then simply rename `index.yaml` to `config.yaml`
+  * If you've defined an `agent_prelude`, rename that field to `agent_session`
+* Convert all JavaScript tools to either Python or Bash
+* For Bash `tools.sh`: Remove the following line:
+  ```bash
+  eval "$(argc --argc-eval "$0" "$@")"
+  ```
+* Any `tools.txt` files you have that define what global functions the agent uses is now replaced by the `global_tools`
+  field in the agent's `config.yaml`. So for example: If your `tools.txt` looks like this:
+  ```text
+  fs_mkdir.sh
+  fs_ls.sh
+  fs_patch.sh
+  fs_cat.sh
+  ```
+  then you need to add the following to your agent's `config.yaml`:
+  ```yaml
+  global_tools:
+    - fs_mkdir.sh
+    - fs_ls.sh
+    - fs_patch.sh
+    - fs_cat.sh
+  ```
+* If you have any bash `tools.sh` that depend on the utility scripts in the `llm-functions` repository, they've been 
+  replaced by built-in utility scripts. So use the following to replace any matching lines in your `tools.sh` files:
+  ```bash
+  ##################
+  ## Scripts file ##
+  ##################
+  ROOT_DIR="${LLM_ROOT_DIR:-$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)}"
+  # replace with
+  source "$LLM_PROMPT_UTILS_FILE"
+  
+  #######################
+  ## guard_path script ##
+  #######################
+  "$ROOT_DIR/utils/guard_path.sh"
+  # replace with
+  guard_path
+  
+  ############################
+  ## guard_operation script ##
+  ############################
+  "$ROOT_DIR/utils/guard_operation.sh"
+  # replace with
+  guard_operation
+  
+  ######################
+  ## patch.awk script ##
+  ######################
+  awk -f "$ROOT_DIR/utils/patch.awk"
+  # replace with
+  patch_file
+  ```
+  
+When you're done with this migration, you should have the following:
+
+* No more `functions/agents` directory
+* No `functions/agents.txt` file (Loki assumes that if the agent directory exists, it is loadable)
+* No `<loki-config-dir>/agents/<agent-name>/tools.txt`
+* No `<loki-config-dir>/agents/<agent-name>/index.yaml`
+
+## Functions
+Loki consolidates much of the `llm-functions` repo functionality into one binary. So this means
+
+* There's no need to have `argc` installed anymore
+* No separate repository to manage
+* No `tools.txt`
+* No `functions.json`
+* No `functions/mcp` directory at all
+* No `functions/scripts`
+
+Here's how to migrate your functions over to Loki from the `llm-functions` repository.
+
+* Copy your AIChat `<aichat-config-dir>/functions` directory into your Loki config directory
+* Delete the following files and directories from your `<loki-config-dir>/functions` directory:
+  * `scripts/`
+  * `agents.txt`
+  * `functions.json`
+  * `Argcfile.sh`
+  * `README.md` (irrelevant now)
+  * `LICENSE` (irrelevant now)
+  * `utils/guard_operation.sh`
+  * `utils/guard_path.sh`
+  * `utils/patch.awk`
+* Everything in `tools.txt` now lives in the global config file under the `visible_tools` setting:
+  ```text
+  get_current_weather.sh
+  execute_command.sh
+  web_search.sh
+  #execute_py_code.py
+  query_jira_issues.sh
+  ```
+  becomes the following in your `<loki-config-dir>/config.yaml`
+  ```yaml
+  visible_tools:
+    - get_current_weather.sh
+    - execute_command.sh
+    - web_search.sh
+    # - web_search.sh
+    - query_jira_issues.sh
+  ```
+* If you've defined a `functions/mcp.json` file, you can leave it alone.
+* Similarly to agents, if you have any bash `tools.sh` that depend on the utility scripts in the `llm-functions` 
+  repository, they've been replaced by built-in utility scripts. So use the following to replace any matching lines in 
+  your `tools.sh` files:
+  ```bash
+  ##################
+  ## Scripts file ##
+  ##################
+  ROOT_DIR="${LLM_ROOT_DIR:-$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)}"
+  # replace with
+  source "$LLM_PROMPT_UTILS_FILE"
+  
+  #######################
+  ## guard_path script ##
+  #######################
+  "$ROOT_DIR/utils/guard_path.sh"
+  # replace with
+  guard_path
+  
+  ############################
+  ## guard_operation script ##
+  ############################
+  "$ROOT_DIR/utils/guard_operation.sh"
+  # replace with
+  guard_operation
+  
+  ######################
+  ## patch.awk script ##
+  ######################
+  awk -f "$ROOT_DIR/utils/patch.awk"
+  # replace with
+  patch_file
+  ```
+  
+Refer to the [custom bash tools docs](./function-calling/CUSTOM-BASH-TOOLS.md) to learn how to compile and test bash 
+tools in Loki without needing to use `argc`.

docs/ENVIRONMENT-VARIABLES.md

@@ -0,0 +1,113 @@
+# Environment Variables
+
+Loki is designed to be highly dynamic and customizable. As a result, Loki utilizes a number of environment variables
+that can be used to modify its behavior at runtime without needing to modify the existing configuration files.
+
+Loki also supports defining environment variables via a `.env` file in the Loki configuration directory. This directory
+varies between systems, so you can find the location of your configuration directory using the following command:
+
+```shell
+loki --info | grep 'config_dir' | awk '{print $2}'
+```
+
+## Quick Links
+<!--toc:start-->
+- [Global Configuration Related Variables](#global-configuration-related-variables)
+- [Client Related Variables](#client-related-variables)
+- [Files and Directory Related Variables](#files-and-directory-related-variables)
+- [Agent Related Variables](#agent-related-variables)
+- [Logging Related Variables](#logging-related-variables)
+- [Miscellaneous Variables](#miscellaneous-variables)
+<!--toc:end-->
+
+---
+
+## Global Configuration Related Variables
+All configuration items in the global config file have environment variables that can be overridden at runtime. To see
+all configuration options and more thorough descriptions, refer to the [example config file](../config.example.yaml).
+
+Below are the most commonly used configuration settings and their corresponding environment variables:
+
+| Setting                    | Environment Variable            |
+|----------------------------|---------------------------------|
+| `model`                    | `LOKI_MODEL`                    |
+| `temperature`              | `LOKI_TEMPERATURE`              |
+| `top_p`                    | `LOKI_TOP_P`                    |
+| `stream`                   | `LOKI_STREAM`                   |
+| `save`                     | `LOKI_SAVE`                     |
+| `editor`                   | `LOKI_EDITOR`                   |
+| `wrap`                     | `LOKI_WRAP`                     |
+| `wrap_code`                | `LOKI_WRAP_CODE`                |
+| `save_session`             | `LOKI_SAVE_SESSION`             |
+| `compression_threshold`    | `LOKI_COMPRESSION_THRESHOLD`    |
+| `function_calling_support` | `LOKI_FUNCTION_CALLING_SUPPORT` |
+| `enabled_tools`            | `LOKI_ENABLED_TOOLS`            |
+| `mcp_server_support`       | `LOKI_MCP_SERVER_SUPPORT`       |
+| `enabled_mcp_servers`      | `LOKI_ENABLED_MCP_SERVERS`      |
+| `rag_embedding_model`      | `LOKI_RAG_EMBEDDING_MODEL`      |
+| `rag_reranker_model`       | `LOKI_RAG_RERANKER_MODEL`       |
+| `rag_top_k`                | `LOKI_RAG_TOP_K`                |
+| `rag_chunk_size`           | `LOKI_RAG_CHUNK_SIZE`           |
+| `rag_chunk_overlap`        | `LOKI_RAG_CHUNK_OVERLAP`        |
+| `highlight`                | `LOKI_HIGHLIGHT`                |
+| `theme`                    | `LOKI_THEME`                    |
+| `serve_addr`               | `LOKI_SERVE_ADDR`               |
+| `user_agent`               | `LOKI_USER_AGENT`               |
+| `save_shell_history`       | `LOKI_SAVE_SHELL_HISTORY`       |
+| `sync_models_url`          | `LOKI_SYNC_MODELS_URL`          |
+
+
+## Client Related Variables
+The following environment variables are available for clients in Loki:
+
+| Environment Variable                   | Description                                                                                                                                                                                                                                             |
+|----------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `{client}_API_KEY`                     | For clients that require an API key, you can define the keys either through environment variables or <br>using the [vault](./VAULT.md). The variables are named after the client to which they apply; <br>e.g. `OPENAI_API_KEY`, `GEMINI_API_KEY`, etc. |
+| `LOKI_PLATFORM`                        | Combine with `{client}_API_KEY` to run Loki without a configuration file. <br>This variable is ignored if a configuration file exists.                                                                                                                  |
+| `LOKI_PATCH_{client}_CHAT_COMPLETIONS` | Patch chat completion requests to models on the corresponding client; Can modify the URL, body, <br>or headers.                                                                                                                                         | 
+| `LOKI_SHELL`                           | Specify the shell that Loki should be using when executing commands                                                                                                                                                                                     |
+
+## Files and Directory Related Variables
+You can also customize the files and directories that Loki loads its configuration files from:
+
+| Environment Variable | Description                                                            | Default Value                   |
+|----------------------|------------------------------------------------------------------------|---------------------------------|
+| `LOKI_CONFIG_DIR`    | Customize the location of the Loki configuration directory.            | `<user-config-dir>/loki`        |
+| `LOKI_ENV_FILE`      | Customize the location of the `.env` file to load at startup.          | `<loki-config-dir>/.env`        |
+| `LOKI_CONFIG_FILE`   | Customize the location of the global `config.yaml` configuration file. | `<loki-config-dir>/config.yaml` |
+| `LOKI_ROLES_DIR`     | Customize the location of the `roles` directory.                       | `<loki-config-dir>/roles`       |
+| `LOKI_SESSIONS_DIR`  | Customize the location of the `sessions` directory.                    | `<loki-config-dir>/sessions`    |
+| `LOKI_RAGS_DIR`      | Customize the location of the `rags` directory.                        | `<loki-config-dir>/rags`        |
+| `LOKI_FUNCTIONS_DIR` | Customize the location of the `functions` directory.                   | `<loki-config-dir>/functions`   |
+
+## Agent Related Variables
+You can also customize the location of full agent configurations using the following environment variables:
+
+| Environment Variable         | Description                                                                                                                         |
+|------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|
+| `<AGENT_NAME>_CONFIG_FILE`   | Customize the location of the agent's configuration file; e.g. `SQL_CONFIG_FILE`                                                    |
+| `<AGENT_NAME>_MODEL`         | Customize the `model` used for the agent; e.g `SQL_MODEL`                                                                           |
+| `<AGENT_NAME>_TEMPERATURE`   | Customize the `temperature` used for the agent; e.g. `SQL_TEMPERATURE`                                                              |
+| `<AGENT_NAME>_TOP_P`         | Customize the `top_p` used for the agent; e.g. `SQL_TOP_P`                                                                          |
+| `<AGENT_NAME>_GLOBAL_TOOLS`  | Customize the `global_tools` that are enabled for the agent (a JSON string array); e.g. `SQL_GLOBAL_TOOLS`                          |
+| `<AGENT_NAME>_MCP_SERVERS`   | Customize the `mcp_servers` that are enabled for the agent (a JSON string array); e.g. `SQL_MCP_SERVERS`                            |
+| `<AGENT_NAME>_AGENT_SESSION` | Customize the `agent_session` used with the agent; e.g. `SQL_SESSION`                                                               |
+| `<AGENT_NAME>_INSTRUCTIONS`  | Customize the `instructions` for the agent; e.g. `SQL_INSTRUCTIONS`                                                                 |
+| `<AGENT_NAME>_VARIABLES`     | Customize the `variables` used for the agent (in JSON format of `[{"key1": "value1", "key2": "value2"}]`); <br>e.g. `SQL_VARIABLES` |
+
+## Logging Related Variables
+The following variables can be used to change the log level of Loki or the location of the log file:
+
+| Environment Variable | Description                                 | Default Value                    |
+|----------------------|---------------------------------------------|----------------------------------|
+| `LOKI_LOG_LEVEL`     | Customize the log level of Loki             | `INFO`                           |
+| `LOKI_LOG_FILE`      | Customize the location of the Loki log file | `<user-cache-dir>/loki/loki.log` |
+
+**Pro-Tip:** You can always tail the Loki logs using the `--tail-logs` flag. If you need to disable color output, you
+can also pass the `--disable-log-colors` flag as well.
+
+## Miscellaneous Variables
+| Environment Variable | Description                                                                                                                                                                                                                                                                                                                    | Default Value |
+|----------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------|
+| `AUTO_CONFIRM`       | Bypass all `guard_*` checks in the bash prompt helpers; useful for agent composition and routing                                                                                                                                                                                                                               |               |
+| `LLM_TOOL_DATA_FILE` | Set automatically by Loki on Windows. Points to a temporary file containing the JSON tool call data. <br>Tool scripts (`run-tool.sh`, `run-agent.sh`, etc.) read from this file instead of command-line args <br>to avoid JSON escaping issues when data passes through `cmd.exe` → bash. **Not intended to be set by users.** |               |

docs/MACROS.md

@@ -0,0 +1,103 @@
+# Macros
+Macros are essentially Loki "scripts"; that is, a predefined sequence of REPL commands that automate repetitive tasks or
+workflows. Macros run in isolated environments, ensuring that the macros don't inherit any pre-existing role, session, 
+RAG, or agent state, and they will not affect your current context. 
+
+This isolation ensures that your workspace remains clean and unaffected by macro operations.
+
+![Macro Example](./images/macros/macros-example.gif)
+
+For more information on Loki's REPL, refer to the [REPL](./REPL.md) documentation.
+
+## Quick Links
+<!--toc:start-->
+- [Macro Definition](#macro-definition)
+  - [Step Definitions](#step-definitions)
+  - [Macro Variables](#macro-variables)
+- [Built-In Macros](#built-in-macros)
+<!--toc:end-->
+
+---
+
+## Macro Definition
+Macros are defined as YAML files in the `macros` subdirectory of your Loki configuration directory. The Loki configuration 
+directory can vary between systems, so to find the location of your macros config directory, you can use the following 
+command:
+
+```shell
+loki --info | grep 'macros_dir' | awk '{print $2}'
+```
+
+Macro definitions are broken into two parts: the `steps` of the macro, and an optional `variables` section that lets 
+users pass in variables to alter the behavior of the macro at runtime.
+
+### Step Definitions
+The step definitions for a macro are straightforward: They are simply the exact commands you would otherwise type in the
+REPL. 
+
+**Example: Macro to generate a git commit message**
+`macros/generate-commit-message.yaml`
+```yaml
+steps:
+  - .file `git diff` -- generate git commit message
+```
+Usage:
+```shell
+$ loki --macro generate-commit-message
+>> .file `git diff` -- generate a git commit message
+Add documentation on macros
+```
+
+For a full example configuration, refer to the [example macro configuration file](../config.macro.example.yaml) in the root of this project.
+
+### Macro Variables
+Sometimes it's useful to be able to modify the behavior of a macro at runtime. This is achieved with the `variables` 
+array of the macro definition.
+
+To pass variables to a macro, since they are just Loki scripts, the syntax is the same as it is for any other scripting 
+language: You just pass them alongside your invocation.
+
+**Example:**
+```shell
+$ loki --macro example-variable-macro first_argument second_argument
+```
+
+Each variable in the `variables` array has the following properties:
+* `name` (Required): the name of the variable, which can be referenced in the actual steps of the macro using the 
+  `{{name}}` syntax.
+* `default` (Optional): A default value for the variable if no value is specified. If no default value is defined, and 
+  no value is provided for the variable at runtime, Loki will error out.
+* `rest` (Optional, Boolean): When set to `true`, this variable will collect all remaining arguments passed to the 
+  macro. This behavior is only applicable when the variable is the last variable in the list. By default, this is 
+  `false`.
+
+The `variables` array is order-dependent; that is to say that all arguments passed to the macro are positional. So be
+careful about the ordering if that is important to your macro's invocation.
+
+**Example: Simple variable example to invoke an agent**
+`macros/invoke-agent.yaml`
+```yaml
+variables:
+  - name: agent                 # No default value means this must be defined at runtime
+  - name: args
+    rest: true                  # All remaining arguments to the macro are collected into this variable
+    default: What can you do?   # This is used if no value is passed at runtime
+steps:
+  - .agent {{agent}}
+  - '{{args}}'
+```
+Usage:
+```shell
+$ loki --macro invoke-agent sql
+# or
+$ loki --macro invoke-agent sql What tables are available?
+```
+
+For a full example configuration, refer to the [example macro configuration file](../config.macro.example.yaml) in the root of this project.
+
+## Built-In Macros
+Loki comes packaged with some useful built-in macros. These are also good examples if you're looking for more examples 
+on how to make your own macros, so be sure to check out the [built-in macro definitions](../assets/macros) if you're 
+looking for more examples.
+
+* `generate-commit-message` - Generate a Git commit message based on the staged changes in the current directory

docs/RAG.md

@@ -0,0 +1,307 @@
+# RAG
+Retrieval Augmented Generation (RAG) is a method of minimizing LLM hallucinations and extending the model's context 
+without consuming a significant portion of the context length. It uses documents and other additional resources that you
+provide to give the model more context for all of your prompts.
+
+Loki has a built-in vector database and full-text search engine to support RAG knowledge bases for your queries.
+
+The generated knowledge bases are stored in the `rag` subdirectory of your Loki configuration directory. The location of 
+this directory varies by system, so you can use the following command to find your RAG directory:
+
+```shell
+loki --info | grep 'rags_dir' | awk '{print $2}'
+```
+
+## Quick Links
+<!--toc:start-->
+- [Usage](#usage)
+  - [Persistent RAG](#persistent-rag)
+  - [Ephemeral RAG](#ephemeral-rag)
+- [How It Works](#how-it-works)
+    - [1. Build](#1-build)
+    - [2. Lookup](#2-lookup)
+    - [2a. Reranking (Optional)](#2a-reranking-optional)
+    - [3. Prompt](#3-prompt)
+- [Supported Document Sources](#supported-document-sources)
+- [Document Loaders](#document-loaders)
+  - [Document Loader Usage](#document-loader-usage)
+- [Advanced Customizations](#advanced-customizations)
+  - [Embedding Model](#embedding-model)
+  - [Reranker](#reranker)
+  - [Chunk Size](#chunk-size)
+    - [Trade-Offs](#chunk-size-trade-offs)
+  - [Chunk Overlap](#chunk-overlap)
+  - [Top K](#top-k)
+    - [Trade-Offs](#top-k-trade-offs)
+  - [RAG Template](#rag-template)
+<!--toc:end-->
+
+---
+
+## Usage
+There's two ways to use RAG in Loki: A persistent RAG that can be loaded on-demand for queries, and an ephemeral one for
+adding RAG to a single specific query.
+
+### Persistent RAG
+In the REPL, persistent RAG is initialized via the `.rag` command:
+
+![Persistent RAG example](./images/rag/persistent-rag.gif)
+
+The generated RAG is then saved to the `rag` subdirectory of the Loki configuration, and can then be loaded whenever you
+want that knowledge base via either `.rag <name>` or `loki --rag <RAG>`.
+
+### Ephemeral RAG
+Short-lived RAG that is only used for a single session or query is loaded using `.file`/`--file`.
+
+You can use it to either execute a prompt from a file, or for temporary RAG. The difference is the usage of the `--` 
+separator. If you only specify a filename and no `--` separator, Loki will know to read the file contents and pass them 
+as a query to the model. Otherwise, the `--` separator is read to indicate that this is the end of the list of documents
+to load into the ephemeral RAG, and what follows is the query to pass to the model.
+
+```shell
+.file prompt.md # Read the file as a prompt
+.file %% -- translate the last reply to italian
+.file `git diff` -- generate a commit message
+```
+
+![Ephemeral RAG Example](./images/rag/ephemeral-rag.gif)
+
+Once the session ends, this RAG will no longer be accessible and is only visible to the current session.
+
+#### The `%%` Document Type
+In addition to the usual documents that can be specified for persistent RAG, ephemeral RAG has a special `%%` value. 
+This value references the content of the last reply. So you can use it like this:
+
+```shell
+.file %% -- translate the last reply to italian
+```
+
+The `--` indicates that this is the end of your documents and the beginning of your prompt.
+
+#### The `cmd` Document Type
+Loki also lets you use command outputs for ephemeral RAG input. Simply enclose the command in backticks:
+
+```shell
+.file `git diff` -- generate a commit message
+```
+
+The `--` indicates that this is the end of your documents and the beginning of your prompt.
+
+## How It Works
+#### 1. Build
+When you define RAG, Loki will first "build" the RAG. This means that Loki will consume the documents you specified and
+generate [embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding) for that text. This essentially just means that Loki translates the document into a language 
+the LLM can understand.
+
+These embeddings are then stored in an in-memory vector database.
+
+#### 2. Lookup
+Loki sits between you and the model. So when you submit a prompt to the model, before Loki ever sends it, it will first 
+convert your prompt into embeddings (LLM language), and look for relevant snippets of text in the vector database.
+
+Loki then passes the top `n`-snippets of text that it finds in the vector database as additional context to the model
+before your prompt.
+
+#### 2a. Reranking (Optional)
+The lookup for relevant snippets of texts uses embeddings to find text that is semantically similar to your prompt, and 
+returns the top `n`-results. This often works fairly well, however these top results aren't always the most relevant for
+answering the specific query.
+
+Reranking improves these initial results (say, the top 20-100 text snippets) and re-scores them using a more 
+sophisticated model. The reranker model will rank documents by their actual usefulness for answering the query to ensure
+the most relevant context is passed to the model alongside your query.
+
+This reranking model can be customized for each RAG you build in Loki. See the [Custom Reranker](#reranker) section
+below for more details on how to customize this.
+
+#### 3. Prompt
+Finally, the text snippets that were looked up in RAG are passed to the model as additional context to your prompt, 
+giving the model query-specific context to answer your question.
+
+## Supported Document Sources
+Loki supports a number of document sources that can be used for RAG:
+
+| Source                   | Example                                                               | Comments                                                                                                                                                 |
+|--------------------------|-----------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Files                    | `/tmp/dir1/file1;/tmp/dir1/file2`                                     |                                                                                                                                                          | 
+| Directory                | `/tmp/dir`                                                            | Picks up all files in a directory and all its subdirectories                                                                                             |
+| Directory (extensions}   | `/tmp/dir2/**/*.{md,txt}`                                             | Finds all files in all subdirectories with the specified extensions                                                                                      |
+| Recursive Filename       | `/tmp/*/LOKI.md`                                                      | The following files will be picked up: <br><ul><li>`/tmp/dir1/LOKI.md`</li><li>`/tmp/dir2/subdir1/LOKI.md`</li><li>`/tmp/dir2/subdir2/LOKI.md`</li></ul> |
+| URL                      | `https://www.ohdsi.org/data-standardization/`                         | Downloads and loads the specified webpage into the <br>knowledge base                                                                                    |
+| Recursive URL (Websites) | `https://github.com/OHDSI/Vocabulary-v5.0/wiki/**`                    | Crawls all pages under the given URL and loads them <br>into the knowledge base                                                                          |
+| Document Loader (custom) | `jina:https://cloud.google.com/bigquery/docs/reference/standard-sql/` | Use a custom document loader to parse the given document                                                                                                 | 
+
+## Document Loaders
+Loki only has built-in support for loading text files. But that functionality can be extended to read all kinds of files
+into your knowledge bases. These custom loaders are used by both RAG and for documents specified using the 
+`.file`/`--file` flags.
+
+In the global configuration file, you can specify loaders for specific document types using the `document_loaders` 
+setting. Each loader is defined by specifying a name and then a command that Loki will execute to load the document.
+
+The following variables are interpolated at runtime by Loki and can be used as placeholders in your command definitions:
+* `$1` (Required) - The input file
+* `$2` (Optional) - The output file. If omitted, `stdout` is used as the output destination
+
+**Note:** It is your responsibility to ensure that any tools used to parse documents into text that Loki can read are 
+installed on your system and are available on your `$PATH`. Loki does not have any built-in way of installing 
+dependencies for document loaders for you.
+
+The following are some example loaders:
+```yaml
+document_loaders:
+  pdf: 'pdftotext $1 -'                                                                 # Use pdftotext to convert a PDF file to text
+                                                                                        # (see https://poppler.freedesktop.org for details on how to install pdftotext)
+  docx: 'pandoc --to plain $1'                                                          # Use pandoc to convert a .docx file to text
+                                                                                        # (see https://pandoc.org for details on how to install pandoc)
+  jina: 'curl -fsSL https://r.jina.ai/$1 -H "Authorization: Bearer {{JINA_API_KEY}}'    # Use Jina to translate a website into text;
+                                                                                        # Requires a Jina API key to be added to the Loki vault
+  git: >                                                                                # Use yek to load a git repository into the knowledgebase (https://github.com/bodo-run/yek)
+    sh -c "yek $1 --json | jq 'map({ path: .filename, contents: .content })'" 
+```
+
+### Document Loader Usage
+Once you have your loaders defined, you can specify when Loki should use them by prefixing any RAG file/directory/URI 
+with the name of the loader.
+
+**Example: Load a git repo into RAG**
+![Git Repo Loader Example](./images/rag/git-loader.png)
+
+**Example: Use pdf loader for ephemeral RAG**
+```shell
+$ loki --file pdf:some-file.pdf
+```
+
+## Advanced Customizations
+For those familiar with RAG, Loki exposes a handful of advanced global settings that can be used to tweak your default
+RAG configurations.
+
+### Embedding Model
+When Loki queries your RAG knowledge bases, it needs to first convert your query into embeddings. By default, Loki uses 
+the same embedding model that was used to create the knowledge base in the first place.
+
+This can be customized to any other embedding model available in your configured clients by setting the 
+`rag_embedding_model` setting in your global Loki configuration file:
+
+```yaml
+rag_embedding_model: null        # Specifies the embedding model used for context retrieval
+```
+
+### Reranker
+By default, Loki uses [Reciprocal Rank Fusion (RRF)](https://www.elastic.co/docs/reference/elasticsearch/rest-apis/reciprocal-rank-fusion) to merge vector and keyword search results.
+
+You can change the default reranker model to any other reranking model in your configured clients. To change the default
+reranker model, simply change the value of the `rag_reranker_model` setting in your global configuration file:
+
+```yaml
+rag_reranker_model: null       # By default, 
+```
+
+### Chunk Size
+In the context of RAG, the chunk size is the maximum length of each text chunk (measured in characters) that is created 
+when splitting documents. In Loki, this defaults to `2000` characters.
+
+You can specify a different global default by setting the `rag_chunk_size` property in your global configuration file:
+
+```yaml
+rag_chunk_size: null             # Defines the size of chunks for document processing in characters
+```
+
+#### Chunk Size Trade-Offs
+Keep in mind the following trade-offs when changing the chunk size:
+
+* **Smaller chunks (e.g. 256 characters):** More precise retrieval, better semantic focus, but may lack context or split
+  important information
+* **Larger chunks (e.g. 1024 characters):** More context preserved, fewer chunks to manage, but less precise matching 
+  and more noise in retrieved document
+
+### Chunk Overlap
+Chunk overlap in RAG is the number of characters that overlap between consecutive chunks to maintain continuity. 
+
+---
+
+**Example:** If the following sentence is cut off at the end of one chunk
+
+`I was doing fine until someone brought up`
+
+You'll ideally want that full sentence to be picked up at the beginning of the next chunk to make sure the full meaning 
+is captured. So in this example, if your chunk overlap is 42 characters, then the start of the next chunk would look
+like this:
+
+`I was doing fine until someone brought up the game. <next sentence>`
+
+---
+
+Often, this value is 10%-20% of the chunk size.
+
+By default, in Loki, this value is 5% the chunk size. You can override this and specify the default chunk overlap (in 
+characters) that Loki should use as a global default by setting the `rag_chunk_overlap` property in the global Loki 
+configuration file:
+
+```yaml
+rag_chunk_overlap: null          # Defines the overlap between chunks
+```
+
+### Top K
+In RAG, `top_k` represents the top `k`-chunks to return from the vector database query. Think of it like if you search 
+something on Google and only care about the top 10 results, that's what you'll use for your context.
+
+In Loki, the default value for this is `5`. You can customize this global default by setting the `rag_top_k` property in
+your global configuration file:
+
+```yaml
+rag_top_k: 5                     # Specifies the number of documents to retrieve for answering queries
+```
+
+#### Top K Trade-Offs
+When customizing this value, keep in mind the following trade-offs so you get the best performance:
+
+* **Lower top_k (e.g. 3):** Faster, more focused context, lower cost, but risks missing relevant information
+* **Higher top_k (e.g. 10):** More comprehensive coverage, but more noise, higher latency, increased token costs, and 
+  potential context window constraints
+
+### RAG Template
+When you use RAG in Loki, after Loki performs the lookup for relevant chunks of text to add as context to your query, it
+will add the retrieved text chunks as context to your query before sending it to the model. The format of this context
+is determined by the `rag_template` setting in your global Loki configuration file.
+
+This template utilizes three placeholders:
+* `__INPUT__`: The user's actual query
+* `__CONTEXT__`: The context retrieved from RAG
+* `__SOURCES__`: A numbered list of the source file paths or URLs that the retrieved context came from
+
+These placeholders are replaced with the corresponding values into the template and make up what's actually passed to 
+the model at query-time. The `__SOURCES__` placeholder enables the model to cite which documents its answer is based on,
+which is especially useful when building knowledge-base assistants that need to provide verifiable references.
+
+The default template that Loki uses is the following:
+
+```text
+Answer the query based on the context while respecting the rules. (user query, some textual context and rules, all inside xml tags)
+
+<context>
+__CONTEXT__
+</context>
+
+<sources>
+__SOURCES__
+</sources>
+
+<rules>
+- If you don't know, just say so.
+- If you are not sure, ask for clarification.
+- Answer in the same language as the user query.
+- If the context appears unreadable or of poor quality, tell the user then answer as best as you can.
+- If the answer is not in the context but you think you know the answer, explain that to the user then answer with your own knowledge.
+- Answer directly and without using xml tags.
+- When using information from the context, cite the relevant source from the <sources> section.
+</rules>
+
+<user_query>
+__INPUT__
+</user_query>
+```
+
+You can customize this template by specifying the `rag_template` setting in your global Loki configuration file. Your 
+template *must* include both the `__INPUT__` and `__CONTEXT__` placeholders in order for it to be valid. The 
+`__SOURCES__` placeholder is optional. If it is omitted, source references will not be included in the prompt.